Cooks in batch for booked events (weddings, functions, corporate) charging per plate; capacity is meals-per-event × events, not fixed seats, with advance deposits funding the food buy and strong wedding-season peaks.
Turnover · metro₹1.75 Cr
Turnover · non-metro₹67.20 L
EBITDA · metro₹21.06 L
Half-width (photo)±404%→61%
Revenue drivers · metro seats × turns/day × avg cover × operating days
| Driver | Class | lo | base | hi | |
|---|
| Plates / event (batch capacity) | Claim | 120 | 250 | 500 | plates |
| Events / serving day | Claim | 0.8 | 1.2 | 2 | events |
| Price / plate | Claim | 250 | 450 | 800 | ₹ |
| Serving days / yr (seasonal) | Claim | 90 | 130 | 180 | days |
Registry: gm [0.38, 0.46, 0.54] · opex [0.28, 0.34, 0.42] · DIO 5d · DSO 25d · DPO 15d · η 1.2
Occupancy — owned vs rented
| Rent/mo · metro | ₹30,000 / ₹60,000 / ₹1.20 L |
| Rent/mo · non-metro | ₹8,000 / ₹18,000 / ₹40,000 |
| Deposit → BS asset | 4 months |
Owned signal: electricity bill in proprietor's name + no rental agreement → owned prep kitchen; substitute a fixed-asset/collateral note (vessels, cooking range, transport) for rent
Balance-sheet build
inventory low & event-driven (bought against confirmed orders) — DIO 4–6d; receivables material (event clients 15–30d, DSO 18–25) partly offset by customer advances (current liability); payables = raw-material suppliers 10–20d; fixed assets = cooking range, bulk vessels, refrigeration, transport, tents/serving gear
Guided capture — photo order
1 exterior 2 kitchen 3 storage 4 machinery 5 event_order_book 6 dispatch 7 qr_code 8 utility_meter 9 gst_board 10 licence 11 pukka_invoice 12 udyam
Next-photo evidence · Eq 7
Event order/booking register (12 months) Observed
Dated bookings give events/yr directly and expose seasonality.
Signed quotation / invoice per plate Observed
Contracted per-plate rate constrains cover across menu tiers.
Kitchen batch capacity (vessels, burners, staff) Observed
Cooking gear + crew size caps plates deliverable per event.
Advance-deposit ledger External
Deposits received corroborate event count and fund the food buy (lowers WCR).
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| GST 3B/GSTR-1 & e-invoice | declared per-event billing vs order-book-derived turnover (concordance) | strong |
| Advance-deposit / bank AA feed | deposits and settlements → event count and receivable pattern; advances = customer liability offsetting WCR | strong |
| UPI/QR settlement | balance payments and smaller orders → banked share | medium |
| FSSAI (catering) licence | legitimacy + declared capacity band | medium |
| Electricity/LPG bill | kitchen load → batch-capacity sanity; power/fuel cost | medium |
| Rental agreement | prep-kitchen rent + deposit (BS) | medium |
Activity signals
| Footfall | n/a — no walk-in footfall; activity = meals dispatched per event captured via dispatch/order book |
| B2B / counterparties | count event counterparties from e-way bills, GSTR-1 line items and the booking register; venue/decorator tie-ups bound event flow; peak concentration in wedding/festival months |
Variance path: Highly seasonal — annualise from the 12-month order book, never from one month; GST + advance ledger + order book concordance → ≤10–15%; photo-only wide (±30–40%) because there are no fixed seats to observe until the booking register lands
Fills seats across meal sittings (peak-hour turns) at a per-head cover, topped up by takeaway and Swiggy/Zomato orders, earning a gross margin over food cost after paying kitchen labour, rent and aggregator commission.
Turnover · metro₹2.04 Cr
Turnover · non-metro₹73.61 L
EBITDA · metro₹28.58 L
Half-width (photo)±132%→27%
Revenue drivers · metro seats × turns/day × avg cover × operating days
| Driver | Class | lo | base | hi | |
|---|
| Covers (seats) | Observed | 30 | 45 | 65 | seats |
| Table turns / day (incl. takeaway equiv.) | Observed | 2 | 3 | 4.2 | turns |
| Average cover / head | Claim | 280 | 420 | 600 | ₹ |
| Operating days / yr | Claim | 350 | 360 | 364 | days |
Registry: gm [0.58, 0.64, 0.7] · opex [0.44, 0.5, 0.56] · DIO 6d · DSO 6d · DPO 15d · η 1.6
Occupancy — owned vs rented
| Rent/mo · metro | ₹60,000 / ₹1.20 L / ₹2.50 L |
| Rent/mo · non-metro | ₹15,000 / ₹32,000 / ₹60,000 |
| Deposit → BS asset | 6 months |
Owned signal: electricity bill in proprietor's name + no rental agreement in Mitra pack → owned; substitute a fixed-asset/collateral note (kitchen fit-out, chillers) for rent
Balance-sheet build
inventory = walk-in/chiller + dry-store worksheet (Observed), perishable so low DIO overrides benchmark; receivables = aggregator settlement float (T+7) only, dine-in cash/UPI ≈ 0; payables = veg daily + grocery 15–30d credit; fixed assets = kitchen equipment, chillers, seating, POS, signage
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 menu_board 5 display 6 storage 7 qr_code 8 aggregator_dashboard 9 utility_meter 10 gst_board 11 licence 12 pukka_invoice 13 kacha_bill 14 udyam
Next-photo evidence · Eq 7
Timed peak-hour occupancy count Observed
Seat count + fill at lunch/dinner peaks bounds usable covers.
POS Z-report day-total & bill count Observed
Bill count / seats gives realised turns; misses no cash covers.
Menu board price sample Observed
Priced menu × typical basket constrains average cover.
Swiggy/Zomato dashboard weekly orders & AOV External
Aggregator AOV and order volume cross-check cover and off-premise share.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| Aggregator settlement (Swiggy/Zomato) | off-premise turns & cover → banked order value net of commission; T+7 settlement drives dso | strong |
| UPI/QR settlement | dine-in & takeaway covers → banked turnover; cash share = residual | strong |
| GST 3B/GSTR-1 | declared turnover (5%/ GST composition) vs photo-derived (concordance) | strong |
| FSSAI licence | legitimacy + declared seating/kitchen scale sanity | medium |
| Electricity bill | connected load (AC + kitchen) → floor/kitchen size; owned/rented; power cost | medium |
| Rental agreement | rent expense + deposit (BS) | medium |
Activity signals
| Footfall | 3–4 timed exterior/interior captures (weekday lunch, weekday dinner peak, weekend dinner) → seat-fill curve; cross-check vs POS bill count and UPI txns |
| B2B / counterparties | aggregator order feed provides a second, independent throughput signal for off-premise sales |
Variance path: Aggregator + UPI + GST concordance → ≤10%; photo-only ≈ ±25–30% until seat-fill curve + POS day-total close the seats/turns gap; cash-heavy standalone eateries stay wider until footfall timed captures land
Serves a very high volume of low-ticket cups of tea (and snacks) from a tiny footprint at near-zero inventory, earning a high per-cup margin over milk/tea/sugar cost with minimal overhead — mostly cash with a QR on the side.
Turnover · metro₹15.20 L
Turnover · non-metro₹6.53 L
EBITDA · metro₹4.56 L
Half-width (photo)±220%→69%
Revenue drivers · metro seats × turns/day × avg cover × operating days
| Driver | Class | lo | base | hi | |
|---|
| Standing/bench service spots | Observed | 4 | 6 | 10 | spots |
| Customers / spot / day (very high) | Claim | 20 | 35 | 55 | turns |
| Average spend / customer | Claim | 12 | 20 | 35 | ₹ |
| Operating days / yr | Claim | 355 | 362 | 365 | days |
Registry: gm [0.52, 0.6, 0.68] · opex [0.24, 0.3, 0.38] · DIO 2d · DSO 0d · DPO 3d · η 0.25
Occupancy — owned vs rented
| Rent/mo · metro | ₹5,000 / ₹12,000 / ₹25,000 |
| Rent/mo · non-metro | ₹1,500 / ₹4,000 / ₹9,000 |
| Deposit → BS asset | 3 months |
Owned signal: no rental agreement + municipal hawker/pitch licence or own frontage in Mitra pack → owned/licensed pitch; substitute a small fixed-asset note (cart, burner, urn) for rent
Balance-sheet build
inventory ≈ nil (milk daily, tea/sugar few days) — DIO 1–2d, ignore benchmark stock build; receivables = 0 (cash/UPI at point of sale); payables = milk vendor 2–5d only; fixed assets = cart/kiosk, LPG burner, urn, benches — collateral-light
Guided capture — photo order
1 exterior 2 neighbourhood 3 display 4 menu_board 5 storage 6 qr_code 7 utility_meter 8 kacha_bill 9 licence 10 udyam
Next-photo evidence · Eq 7
Timed cups-per-hour count (morning + evening peaks) Observed
Counted cups over two peak hours extrapolated to day bounds the very-high turns.
Daily milk intake (litres) from vendor slip External
Litres/day ÷ millilitres per cup back-solves daily cups independent of till.
Rate card / menu board price sample Observed
Tea + snack prices constrain the tiny average spend.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| UPI/QR settlement | partial banked cups → sets a floor; large residual expected (cash-heavy) | medium |
| Milk/dairy vendor supply slip | daily litres → cups/day back-check; strongest volume signal here | strong |
| Electricity/LPG receipt | single burner/light load → micro-scale sanity; power cost negligible | medium |
| Municipal hawker/FSSAI petty licence | legitimacy + pitch tenure | medium |
| GST | usually below threshold / unregistered → GST absent is itself a scale signal | weak |
Activity signals
| Footfall | 2 timed captures at morning (7–10am) and evening (4–7pm) peaks → cups-per-hour curve; cross-check vs milk intake and QR count |
| B2B / counterparties | n/a (pure B2C micro-retail of prepared tea) |
Variance path: Cash-heavy → UPI concordance weak; milk-intake back-check + timed cups-per-hour count are what drive turns half-width to ≤20%; price photo pins the tiny cover; GST usually absent
Delivers a recurring number of subscribed home-style meals per day (lunch and/or dinner) at a low per-meal price, billed monthly, run from a home kitchen or small unit at low overhead — revenue is subscriber count × meals/day.
Turnover · metro₹33.26 L
Turnover · non-metro₹16.77 L
EBITDA · metro₹4.66 L
Half-width (photo)±272%→31%
Revenue drivers · metro seats × turns/day × avg cover × operating days
| Driver | Class | lo | base | hi | |
|---|
| Subscribers / meals dispatched per day | Observed | 40 | 90 | 200 | tiffins |
| Meals / subscriber / day (lunch+dinner) | Claim | 1 | 1.4 | 2 | meals |
| Price / meal | Claim | 50 | 80 | 130 | ₹ |
| Operating days / yr | Claim | 300 | 330 | 360 | days |
Registry: gm [0.36, 0.44, 0.52] · opex [0.24, 0.3, 0.36] · DIO 4d · DSO 22d · DPO 12d · η 0.9
Occupancy — owned vs rented
| Rent/mo · metro | ₹8,000 / ₹18,000 / ₹40,000 |
| Rent/mo · non-metro | ₹3,000 / ₹7,000 / ₹15,000 |
| Deposit → BS asset | 3 months |
Owned signal: residential electricity bill in proprietor's name + no commercial rental agreement → home kitchen (owned); substitute a small fixed-asset note (cooking range, containers, cycle/scooter) for rent
Balance-sheet build
inventory low (bought against known daily demand) — DIO 3–5d; receivables material because billing is monthly-in-arrears (DSO 18–22) though some plans prepay (advance = current liability); payables = grocer/dairy 7–15d; fixed assets = cooking range, tiffin carriers/containers, delivery cycle/scooter — collateral-light
Guided capture — photo order
1 exterior 2 kitchen 3 storage 4 dispatch 5 subscription_register 6 menu_board 7 qr_code 8 utility_meter 9 gst_board 10 licence 11 udyam
Next-photo evidence · Eq 7
Subscriber / route roster (current month) Observed
Active subscriber list gives daily tiffins directly.
Timed dispatch / dabba count at pack-out Observed
Counted meal boxes leaving the kitchen corroborate the roster.
Monthly plan rate card Observed
Monthly plan ÷ meals served back-solves per-meal price.
Monthly billing / UPI receipts ledger External
Recurring collections confirm lunch-vs-both split and receivable days.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| UPI/QR & AA bank feed | monthly subscription collections → subscriber count & banked turnover; recurring pattern is the key signal | strong |
| Subscriber roster / delivery route | daily tiffins (seats) and lunch/dinner split | strong |
| GST | often below threshold; where filed, monthly turnover concordance | medium |
| FSSAI registration/licence | legitimacy + kitchen scale | medium |
| Electricity/LPG bill | cooking load → meals-capacity sanity; fuel cost | medium |
| Rental agreement | unit rent + deposit (BS) where not a home kitchen | weak |
Activity signals
| Footfall | n/a — no walk-in; activity = meals dispatched per day, captured via a timed dabba/box count at midday pack-out |
| B2B / counterparties | corporate/hostel tie-ups (bulk subscriptions) counted from the roster and monthly invoices bound the higher end of subscriber count |
Variance path: Recurring model → subscriber roster + monthly UPI collections drive seats half-width to ≤20% fast; churn is the main variance; photo-only ≈ ±25–30% until the roster and a timed dispatch count land; GST often absent at this scale
Buys seasonal farm commodity in bulk, cleans/mills/expels it into graded finished goods (dal, ground spice, oil + cake), and earns a thin commodity conversion margin on high throughput sold to wholesalers and traders.
Turnover · metro₹11.28 Cr
Turnover · non-metro₹6.25 Cr
EBITDA · metro₹56.38 L
Half-width (photo)±91%→39%
Revenue drivers · metro units/hr × productive hrs/day × utilisation × price/unit × operating days
| Driver | Class | lo | base | hi | |
|---|
| Finished output / hr | Observed | 450 | 600 | 780 | kg/hr |
| Productive hrs / day | Claim | 10 | 12 | 14 | hr |
| Capacity utilisation | Derived | 0.48 | 0.6 | 0.72 | fraction |
| Realisation / kg finished | Claim | 78 | 90 | 104 | ₹/kg |
| Operating days / yr | Claim | 265 | 290 | 305 | days |
Registry: gm [0.09, 0.12, 0.16] · opex [0.05, 0.07, 0.09] · DIO 55d · DSO 30d · DPO 28d · η 1.6
Occupancy — owned vs rented
| Rent/mo · metro | ₹40,000 / ₹80,000 / ₹1.50 L |
| Rent/mo · non-metro | ₹12,000 / ₹25,000 / ₹50,000 |
| Deposit → BS asset | 6 months |
Owned signal: Udyam + DISCOM bill in proprietor/firm name and no rental agreement → owned industrial shed; substitute fixed-asset/collateral note (plant + land) for rent
Balance-sheet build
Inventory splits RM (seasonal grain/seed, largest and most volatile) + WIP (in-process/settling) + FG (bagged) via stock worksheet, overriding benchmark DIO at peak; receivables = trader credit 25–35d; payables = commission-agent/farmer credit 20–30d; fixed assets = expeller/pulveriser/cleaning line, silos, weighbridge, shed
Guided capture — photo order
1 exterior 2 machinery 3 nameplate 4 storage 5 rm_silo 6 wip_floor 7 weighbridge 8 dispatch 9 utility_meter 10 production_register 11 gst_board 12 pukka_invoice 13 licence 14 udyam
Next-photo evidence · Eq 7
Mill/expeller nameplate rating Observed
Installed kg/hr rating caps throughput; cross-check with power draw.
DISCOM demand + monthly units External
kWh vs nameplate load estimates true running utilisation (eta proxy).
Shift / grinding register Claim
Daily start-stop entries bound productive hours through the season.
Finished-goods sale invoice sample External
₹/kg realisation net of by-product credit.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| E-way bills (inward + outward) | RM procurement tonnage & finished dispatch → throughput ceiling and B2B counterparty count | strong |
| GST GSTR-1/3B | declared turnover vs photo-derived output × price (concordance) | strong |
| Electricity/DISCOM bill | connected load + kWh → running utilisation and processing intensity (eta) | strong |
| AA bank feed | commodity purchase outflows & sale receipts → seasonal working-capital swing | medium |
| Rental agreement / Udyam | owned-vs-rented, deposit (BS), plant registration | medium |
Activity signals
| Footfall | n/a (B2B commodity processor) |
| B2B / counterparties | Count distinct buyer GSTINs on GSTR-1 and inward supplier GSTINs on e-way bills; dispatch register + weighbridge slips bound daily tonnage out and reconcile against uph × hours |
Variance path: Energy proxy is strong here: kWh vs nameplate load closes the util/uph gap fast; e-way + GST concordance → ≤12%. Photo-only ≈ ±25–30% until nameplate + power reading + shift log land, mainly due to seasonal utilisation swing
Order-driven workshop where semi-skilled carpenters convert timber and board into furniture and fittings against customer orders; revenue is labour-and-material value added per piece, so output tracks how many orders are on hand (utilisation) rather than steady-state production.
Turnover · metro₹45.36 L
Turnover · non-metro₹24.16 L
EBITDA · metro₹4.54 L
Half-width (photo)±219%→54%
Revenue drivers · metro units/hr × productive hrs/day × utilisation × price/unit × operating days
| Driver | Class | lo | base | hi | |
|---|
| Finished-piece equivalents / productive hour | Observed | 0.5 | 0.9 | 1.4 | units/hr |
| Productive bench-hours / day | Observed | 7 | 8 | 10 | hrs |
| Order-fill utilisation | Claim | 0.4 | 0.6 | 0.78 | fraction |
| Blended realisation / piece | Claim | 2000 | 3500 | 6000 | ₹ |
| Operating days / yr | Claim | 280 | 300 | 315 | days |
Registry: gm [0.28, 0.35, 0.42] · opex [0.2, 0.25, 0.32] · DIO 35d · DSO 20d · DPO 25d · η 0.5
Occupancy — owned vs rented
| Rent/mo · metro | ₹10,000 / ₹22,000 / ₹45,000 |
| Rent/mo · non-metro | ₹4,000 / ₹8,000 / ₹16,000 |
| Deposit → BS asset | 4 months |
Owned signal: electricity bill in proprietor's name + no rental agreement → owned/family premises; replace rent with fixed-asset note (shed + tools)
Balance-sheet build
Inventory = timber/ply/board/hardware RM + WIP half-built pieces (worksheet overrides DIO) + small FG; receivables low as advances are taken; customer advances on made-to-order work are a real liability; payables = timber supplier 20–30d; fixed assets = table saw, planer, router, hand tools, workbenches.
Guided capture — photo order
1 exterior 2 interior 3 machinery 4 tool_rack 5 storage 6 wip_zone 7 dispatch 8 qr_code 9 utility_meter 10 pukka_invoice 11 kacha_bill 12 gst_board 13 order_book 14 udyam 15 licence
Next-photo evidence · Eq 7
Order pad / advance-booking register Observed
Live orders vs bench count fixes utilisation, the key swing.
Recent job invoice / estimate slip Claim
Anchors blended realisation per piece (often kachha).
Carpenter headcount at benches Observed
Hands on benches cap finished-piece output rate.
Timber / board stock stack photo Observed
Standing RM depth signals sustained working days vs sporadic operation.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| UPI/QR settlement | advances & retail receipts → banked share; cash residual is high | medium |
| GST 3B/GSTR-1 | declared turnover if registered (many are composition/unregistered → weaker) | weak |
| Electricity bill | light connected load → premises size sanity; owned/rented | medium |
| Timber purchase bills | RM inflow → output capacity & DIO | medium |
| Rental agreement | rent expense + deposit (BS) | medium |
Activity signals
| Footfall | Mostly n/a; occasional walk-in enquiries — one timed exterior capture only if a display frontage exists |
| B2B / counterparties | Estimate forward pipeline from the order pad; interior-fit contractors and shops are repeat buyers — count them from invoices/UPI counterparties where present. Expect a large cash-sale share a bank feed misses. |
Variance path: Cash-heavy and often unregistered → pack concordance weak; expect ±30–35% photo-only, tightening to ≈15–18% once order pad, headcount and invoice sample close utilisation, uph and price.
Order-driven B2B job-shop that sells billable machine-and-labour hours: customers bring drawings or components, the shop machines/fabricates to spec and invoices per job, so revenue swings with how much of installed machine capacity is booked (utilisation).
Turnover · metro₹88.45 L
Turnover · non-metro₹47.76 L
EBITDA · metro₹10.61 L
Half-width (photo)±154%→40%
Revenue drivers · metro units/hr × productive hrs/day × utilisation × price/unit × operating days
| Driver | Class | lo | base | hi | |
|---|
| Billable jobs / machine-hour (shop aggregate) | Observed | 1.2 | 1.8 | 2.5 | jobs/hr |
| Productive machine-hours / day | Observed | 7 | 9 | 11 | hrs |
| Order-fill utilisation of capacity | Claim | 0.45 | 0.65 | 0.8 | fraction |
| Blended realisation / job | Claim | 1800 | 2800 | 4200 | ₹ |
| Operating days / yr | Claim | 280 | 300 | 315 | days |
Registry: gm [0.32, 0.4, 0.48] · opex [0.22, 0.28, 0.34] · DIO 30d · DSO 45d · DPO 30d · η 1.3
Occupancy — owned vs rented
| Rent/mo · metro | ₹18,000 / ₹35,000 / ₹70,000 |
| Rent/mo · non-metro | ₹6,000 / ₹12,000 / ₹25,000 |
| Deposit → BS asset | 6 months |
Owned signal: DISCOM bill in proprietor's name + no rental agreement in Mitra pack → owned shed; substitute a fixed-asset/collateral note (shed + machines) for rent
Balance-sheet build
Inventory = bar/plate RM stock + WIP jobs on the floor (worksheet overrides DIO) + minimal FG (dispatched to order); receivables material — B2B credit 30–60d drives WCR; customer advances on large jobs sit as a liability; payables = steel supplier 30d; fixed assets = lathes, CNC, milling, welding sets, tooling.
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 machinery 5 tool_rack 6 storage 7 wip_zone 8 dispatch 9 utility_meter 10 pukka_invoice 11 kacha_bill 12 gst_board 13 order_book 14 udyam 15 licence
Next-photo evidence · Eq 7
Open order book / job register Observed
Booked jobs vs machine count fixes utilisation, the dominant swing.
Recent per-job invoice sample External
Cross-checks blended realisation per job against e-invoice.
Machine / lathe / CNC count with plates Observed
Installed spindles cap achievable jobs/hour.
Attendance / shift muster Claim
Confirms single vs double shift → productive hours/day.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| GST 3B/GSTR-1 | declared B2B turnover vs photo-derived (concordance); output-tax base | strong |
| E-way / e-invoice | dispatch value & B2B counterparty count → throughput ceiling | strong |
| Electricity bill | connected load & kWh → machine count and eta triangulation (Eq 8) | medium |
| AA bank feed | B2B receipts vs invoiced sales; receivable ageing (dso) | medium |
| Rental agreement | shed rent expense + deposit (BS) | medium |
Activity signals
| Footfall | n/a (B2B job-shop, no walk-in footfall) |
| B2B / counterparties | Count distinct customers from GSTR-1 / e-way counterparties and open order-book lines; repeat OEM/contractor buyers bound sustainable throughput and receivable concentration |
Variance path: GST + e-way + AA concordance pulls turnover half-width to ≤10–12%; photo-only ≈ ±30% until order book + machine count close the utilisation and uph gaps.
Order-driven steel fabrication: MS bar, pipe and sheet are cut, welded and finished into gates, grills, sheds and structures priced per kilogram or per job, part in-workshop and part on-site, so output and power draw both track how many jobs are booked (utilisation).
Turnover · metro₹55.65 L
Turnover · non-metro₹35.19 L
EBITDA · metro₹3.90 L
Half-width (photo)±149%→40%
Revenue drivers · metro units/hr × productive hrs/day × utilisation × price/unit × operating days
| Driver | Class | lo | base | hi | |
|---|
| Steel fabricated / productive hour (shop aggregate) | Observed | 13 | 22 | 32 | kg/hr |
| Productive welding-hours / day | Observed | 7 | 8.5 | 10 | hrs |
| Order-fill utilisation | Claim | 0.42 | 0.62 | 0.8 | fraction |
| Blended realisation / kg (incl. labour) | Claim | 110 | 160 | 220 | ₹/kg |
| Operating days / yr | Claim | 285 | 300 | 315 | days |
Registry: gm [0.26, 0.33, 0.4] · opex [0.2, 0.26, 0.32] · DIO 28d · DSO 40d · DPO 25d · η 2.6
Occupancy — owned vs rented
| Rent/mo · metro | ₹8,000 / ₹18,000 / ₹40,000 |
| Rent/mo · non-metro | ₹3,500 / ₹7,000 / ₹15,000 |
| Deposit → BS asset | 4 months |
Owned signal: power bill in proprietor's name + no rental agreement → owned yard/shed; substitute fixed-asset/collateral note (yard + welding plant) for rent
Balance-sheet build
Inventory = MS bar/pipe/sheet RM + WIP fabricated gates/structures (worksheet overrides DIO) + FG awaiting dispatch/installation; receivables B2B plus site retention drive WCR (dso 35–50d); material advances from customers are a liability; payables = steel supplier 20–30d; fixed assets = welding sets, cutting/grinding machines, drill, compressor.
Guided capture — photo order
1 exterior 2 interior 3 machinery 4 tool_rack 5 storage 6 wip_zone 7 dispatch 8 utility_meter 9 pukka_invoice 10 kacha_bill 11 gst_board 12 order_book 13 udyam 14 licence
Next-photo evidence · Eq 7
Job order book / site work orders Observed
Booked gate/grill/structure jobs vs plant fixes utilisation.
Per-kg / per-job invoice sample External
Anchors blended ₹/kg realisation.
Welding-set / cutter count with rating Observed
Number & rating of welding sets cap kg/hour throughput.
Monthly kWh from DISCOM bill External
Welding kWh is a strong proxy for productive hours (Eq 8 eta triangulation).
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| GST 3B/GSTR-1 | declared B2B/contractor turnover vs photo-derived (concordance) | strong |
| E-way / e-invoice | dispatched fabricated value & site counterparties → throughput | strong |
| Electricity bill | welding load & kWh → uph and eta triangulation (very informative) | strong |
| Steel purchase bills | MS RM inflow (kg) → output capacity & material cost | medium |
| AA bank feed | B2B receipts, retention/advances vs invoiced (dso) | medium |
Activity signals
| Footfall | n/a (order/site-driven; no retail footfall) |
| B2B / counterparties | Estimate builders/contractors and site jobs from e-way bills, GSTR-1 counterparties and the work-order book; welding kWh draw curve corroborates active fabrication days versus idle. |
Variance path: Power bill + e-way + GST triangulate tightly (welding is energy-heavy) → ≤10–12%; photo-only ≈ ±30% until order book, plant count and kWh close utilisation, uph and hours.
Mills wheat into atta/maida or paddy into rice at high tonnage, selling primary output to wholesalers plus by-products (bran, husk, broken grain); a very thin per-kg milling margin on large volume, with power a key cost.
Turnover · metro₹14.52 Cr
Turnover · non-metro₹7.82 Cr
EBITDA · metro₹58.06 L
Half-width (photo)±104%→37%
Revenue drivers · metro units/hr × productive hrs/day × utilisation × price/unit × operating days
| Driver | Class | lo | base | hi | |
|---|
| Milled output / hr | Observed | 1200 | 1800 | 2500 | kg/hr |
| Productive hrs / day | Claim | 11 | 14 | 16 | hr |
| Capacity utilisation | Derived | 0.48 | 0.6 | 0.72 | fraction |
| Blended realisation / kg | Claim | 27 | 32 | 38 | ₹/kg |
| Operating days / yr | Claim | 275 | 300 | 320 | days |
Registry: gm [0.06, 0.09, 0.12] · opex [0.035, 0.05, 0.07] · DIO 45d · DSO 22d · DPO 22d · η 2
Occupancy — owned vs rented
| Rent/mo · metro | ₹40,000 / ₹85,000 / ₹1.60 L |
| Rent/mo · non-metro | ₹12,000 / ₹28,000 / ₹55,000 |
| Deposit → BS asset | 6 months |
Owned signal: Udyam + DISCOM bill + weighbridge on-site in firm name, no lease → owned mill; substitute plant/land collateral note for rent
Balance-sheet build
Inventory = RM (grain, largest line, seasonal + MSP-linked) + minimal WIP (short mill cycle) + FG (bagged flour/rice) + by-product stock; DIO worksheet overrides benchmark at harvest peak; receivables low 15–25d (part-cash wholesale); payables 15–25d; fixed assets = roller/huller line, silos, weighbridge, packing, DG set
Guided capture — photo order
1 exterior 2 machinery 3 nameplate 4 rm_silo 5 storage 6 wip_floor 7 weighbridge 8 dispatch 9 utility_meter 10 production_register 11 gst_board 12 pukka_invoice 13 licence 14 udyam
Next-photo evidence · Eq 7
Roller-body/huller nameplate (TPH) Observed
Rated tonnes-per-hour caps milling throughput.
Monthly kWh vs connected load External
Specific energy per tonne fixes running utilisation (eta proxy).
Weighbridge slip book (in/out tonnage) Observed
Daily tonnage in/out bounds running hours and yield.
Atta/rice + by-product sale invoice External
Blended ₹/kg incl. bran/husk credit.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| E-way bills | grain inward & flour/rice outward tonnage → throughput and buyer count | strong |
| GST GSTR-1/3B | declared turnover vs output × blended price (note exempt/branded mix) | strong |
| Electricity/DISCOM bill | kWh per tonne → utilisation and processing intensity (eta) | strong |
| Weighbridge log | daily in/out tonnage → capacity realisation | strong |
| AA bank feed / rental / Udyam | receipts, owned-vs-rented, deposit, registration | medium |
Activity signals
| Footfall | n/a (B2B miller) |
| B2B / counterparties | Buyer GSTIN count on GSTR-1 (wholesalers, government procurement) and grain-supplier GSTINs on inward e-way bills; weighbridge + dispatch register give ground-truth daily tonnage to bound uph × hours × util |
Variance path: Very low margin means turnover certainty is what matters: weighbridge tonnage + power (kWh/tonne) + e-way concordance → ≤12%. Watch GST exempt/branded split when reconciling declared vs derived turnover
Batch-fries/roasts and packs branded namkeen and snacks under FSSAI licence, selling through distributors and retail; earns a healthier brand margin than commodity processors but carries distribution, marketing and shelf-life costs.
Turnover · metro₹7.00 Cr
Turnover · non-metro₹3.83 Cr
EBITDA · metro₹62.99 L
Half-width (photo)±110%→35%
Revenue drivers · metro units/hr × productive hrs/day × utilisation × price/unit × operating days
| Driver | Class | lo | base | hi | |
|---|
| Packed output / hr | Observed | 120 | 180 | 250 | kg/hr |
| Productive hrs / day | Claim | 10 | 12 | 14 | hr |
| Line utilisation | Derived | 0.48 | 0.6 | 0.73 | fraction |
| Realisation / kg (wholesale) | Claim | 150 | 180 | 220 | ₹/kg |
| Operating days / yr | Claim | 280 | 300 | 318 | days |
Registry: gm [0.2, 0.27, 0.34] · opex [0.14, 0.18, 0.23] · DIO 32d · DSO 30d · DPO 28d · η 1.9
Occupancy — owned vs rented
| Rent/mo · metro | ₹50,000 / ₹1.00 L / ₹2.00 L |
| Rent/mo · non-metro | ₹18,000 / ₹40,000 / ₹80,000 |
| Deposit → BS asset | 6 months |
Owned signal: FSSAI address + DISCOM bill + Udyam in firm name, no lease → owned unit; substitute plant/cold-store collateral note for rent
Balance-sheet build
Inventory = RM (besan/oil/spices/packaging film) + WIP (fried/seasoning stage) + FG (packed, shelf-life-limited so DIO capped low) via worksheet; receivables = distributor credit 20–40d; payables = RM/packaging credit 20–35d; fixed assets = fryers, ovens, mixers, auto-packing machines, cold/dry store, brand/trademark (intangible)
Guided capture — photo order
1 exterior 2 machinery 3 nameplate 4 storage 5 wip_floor 6 display 7 dispatch 8 utility_meter 9 production_register 10 gst_board 11 pukka_invoice 12 licence 13 udyam
Next-photo evidence · Eq 7
Auto-packing machine pouch counter Observed
Pouches/min × pack weight fixes packed kg/hr.
Daily batch / production sheet Claim
Batches/day × batch size bounds hours and line utilisation.
Distributor tax invoice + price list External
Net wholesale ₹/kg after scheme/margin.
Monthly kWh (fryer/oven load) External
Frying/roasting energy corroborates running utilisation (eta).
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| GST GSTR-1/3B | declared turnover vs output × price; distributor spread | strong |
| E-way bills | RM (besan/oil/spice) inward & FG dispatch to distributors → throughput and distributor count | strong |
| AA bank / UPI-QR settlement | distributor collections + counter cash sales → banked vs cash split | medium |
| Electricity bill | fryer/oven + packing load → utilisation and eta | medium |
| FSSAI licence / Udyam / rental | licensed capacity, owned-vs-rented, deposit | medium |
Activity signals
| Footfall | Optional: factory-outlet counter footfall via 2–3 timed captures cross-checked with counter UPI/QR; minor vs wholesale |
| B2B / counterparties | Distributor/retailer GSTIN count on GSTR-1 and RM-supplier GSTINs on inward e-way; dispatch register cartons/day + packing counter bound daily packed output |
Variance path: Brand margin makes price the sensitive driver — distributor invoice net of schemes matters. Energy proxy moderate (frying load). GST + e-way + UPI concordance → ≤12%; watch cash-counter share a bank feed misses. Photo-only ≈ ±25% until packing counter + batch log land
Larger workshop making both batch stock lines and custom furniture from wood, board and foam, often with a showroom carrying finished stock; revenue blends made-to-stock production with made-to-order jobs, so utilisation stays higher than pure job-work but finished-goods inventory is heavy.
Turnover · metro₹2.05 Cr
Turnover · non-metro₹1.34 Cr
EBITDA · metro₹24.64 L
Half-width (photo)±207%→39%
Revenue drivers · metro units/hr × productive hrs/day × utilisation × price/unit × operating days
| Driver | Class | lo | base | hi | |
|---|
| Finished-piece equivalents / hour (line aggregate) | Observed | 1.2 | 2 | 3.2 | units/hr |
| Productive shop-hours / day | Observed | 8 | 9 | 11 | hrs |
| Capacity utilisation (batch + order) | Claim | 0.48 | 0.68 | 0.84 | fraction |
| Blended realisation / piece | Claim | 3500 | 5500 | 9500 | ₹ |
| Operating days / yr | Claim | 290 | 305 | 320 | days |
Registry: gm [0.34, 0.42, 0.5] · opex [0.24, 0.3, 0.36] · DIO 60d · DSO 25d · DPO 30d · η 1.2
Occupancy — owned vs rented
| Rent/mo · metro | ₹30,000 / ₹60,000 / ₹1.20 L |
| Rent/mo · non-metro | ₹10,000 / ₹20,000 / ₹40,000 |
| Deposit → BS asset | 6 months |
Owned signal: electricity bill in proprietor's name + no rental agreement for the workshop → owned; showroom on high street is usually rented even when the shed is owned — check both premises separately
Balance-sheet build
Inventory is the heaviest of the four — RM (board/ply/wood/foam/hardware) + WIP + finished-goods stock in showroom/warehouse (DIO 55–65d); receivables = dealer credit + retail EMI (dso 20–30d); customer advances on custom orders are a liability; payables = board/foam suppliers 28–35d; fixed assets = panel saw, edge-bander, CNC router, spray booth, showroom fit-out.
Guided capture — photo order
1 exterior 2 showroom 3 interior 4 machinery 5 tool_rack 6 storage 7 wip_zone 8 display 9 dispatch 10 price_board 11 utility_meter 12 pukka_invoice 13 gst_board 14 order_book 15 udyam 16 licence
Next-photo evidence · Eq 7
Production plan / order + dealer book Observed
Batch plan + open orders vs line capacity fixes utilisation.
Invoice / showroom price-board sample External
Anchors blended realisation across stock and custom lines.
Panel saw / edge-bander / router count Observed
Installed machine line caps finished-piece output rate.
Shift muster / attendance Claim
Confirms single vs double shift → productive hours/day.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| GST 3B / e-invoice / e-way | declared turnover, dealer dispatches & counterparties (concordance) | strong |
| UPI/QR settlement | showroom retail sales & advances → banked share | strong |
| Electricity bill | machine load (saw/bander/spray) → uph & eta sanity | medium |
| RM purchase bills (board/foam) | material inflow → output capacity, gm & DIO | medium |
| Rental agreement | workshop + showroom rent & deposit (BS) | medium |
Activity signals
| Footfall | 3 timed showroom captures (weekday evening, weekend) → walk-in curve; cross-check vs UPI/POS retail count and conversion to orders |
| B2B / counterparties | Count dealer/interior-contractor counterparties from GSTR-1 & e-way; dealer credit and order backlog bound sustainable batch throughput |
Variance path: GST + e-way + UPI concordance → ≤10%; photo-only ≈ ±25–30% until production plan, machine count and price sample close utilisation, uph and price. FG showroom stock must be worksheet-counted — benchmark DIO alone understates it.
Runs banks of sewing machines with operators to stitch garments on a cut-make-trim job-work basis for principals/exporters who supply the fabric; earns a per-piece stitching charge, so revenue is labour-and-machine driven with very low owned raw material.
Turnover · metro₹2.46 Cr
Turnover · non-metro₹1.40 Cr
EBITDA · metro₹24.57 L
Half-width (photo)±119%→33%
Revenue drivers · metro units/hr × productive hrs/day × utilisation × price/unit × operating days
| Driver | Class | lo | base | hi | |
|---|
| Pieces stitched / hr (all lines) | Observed | 300 | 450 | 620 | pieces/hr |
| Productive hrs / day | Claim | 8.5 | 10 | 11.5 | hr |
| Line/operator utilisation | Derived | 0.56 | 0.7 | 0.82 | fraction |
| Job-work rate / piece | Claim | 18 | 26 | 36 | ₹/piece |
| Operating days / yr | Claim | 285 | 300 | 312 | days |
Registry: gm [0.28, 0.36, 0.45] · opex [0.2, 0.26, 0.32] · DIO 18d · DSO 42d · DPO 15d · η 0.9
Occupancy — owned vs rented
| Rent/mo · metro | ₹45,000 / ₹90,000 / ₹1.80 L |
| Rent/mo · non-metro | ₹16,000 / ₹35,000 / ₹70,000 |
| Deposit → BS asset | 6 months |
Owned signal: DISCOM bill + Udyam in firm name, no lease → owned shed; substitute machinery/shed collateral note for rent (machines are the main fixed asset)
Balance-sheet build
Owned inventory is LOW — principal supplies fabric (held as non-owned job-work stock, off balance sheet); owned inventory ≈ thread/trims/packaging + WIP (bundles in-line) only → short DIO; receivables = principal credit 35–50d (concentration risk); payables low 10–20d (consumables); fixed assets = sewing/overlock/flatlock machines, cutting tables, pressing (machines are primary collateral)
Guided capture — photo order
1 exterior 2 machinery 3 interior 4 wip_floor 5 dispatch 6 utility_meter 7 production_register 8 gst_board 9 pukka_invoice 10 licence 11 udyam
Next-photo evidence · Eq 7
Installed machines × occupied operator seats Observed
Sewing machines × seated operators × per-operator rate sets piece throughput.
Job-work challan / invoice (pieces × rate) External
Per-piece CMT rate and style mix from principal billing.
Daily dispatch / bundle-completion register Observed
Pieces completed/day reveals true line utilisation and absenteeism drag.
Operator attendance / shift board Claim
Present operators × hours bound effective productive hours.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| Job-work challans / e-way (fabric in, garments out) | principal-supplied fabric inward & stitched dispatch → pieces handled and principal count | strong |
| GST GSTR-1/3B (SAC job-work) | declared job-work receipts vs pieces × rate (concordance) | strong |
| AA bank feed | principal payments (often 1–3 principals → concentration) vs invoiced pieces | strong |
| Electricity bill | low load (light sewing) → utilisation sanity, owned-vs-rented; eta is low | medium |
| Rental / Udyam / factory licence | machine count, worker band, owned-vs-rented, deposit | medium |
Activity signals
| Footfall | n/a (B2B job-worker) |
| B2B / counterparties | Principal GSTIN count is small (1–3) → concentration risk read from GSTR-1 and inward fabric challans; occupied-seat count × line balance and dispatch register bound pieces/day and cross-check pieces × rate against banked receipts |
Variance path: Labour-driven, low-energy: energy proxy is weaker here (light sewing load), so machine/operator count + dispatch register are the decisive signals for uph/util. Job-work challans + GST(SAC) + bank concordance → ≤12%; flag principal concentration for receivables and revenue-continuity risk
Converts polymer granules into moulded/extruded packaging (containers, films, bags, pouches) on power-hungry machines against B2B purchase orders, earning a conversion margin per kg over the granule and power cost.
Turnover · metro₹7.99 Cr
Turnover · non-metro₹4.52 Cr
EBITDA · metro₹55.91 L
Half-width (photo)±101%→35%
Revenue drivers · metro units/hr × productive hrs/day × utilisation × price/unit × operating days
| Driver | Class | lo | base | hi | |
|---|
| Converted output / hr | Observed | 80 | 120 | 165 | kg/hr |
| Productive hrs / day | Claim | 16 | 20 | 22 | hr |
| Machine utilisation | Derived | 0.52 | 0.65 | 0.78 | fraction |
| Realisation / kg converted | Claim | 135 | 160 | 195 | ₹/kg |
| Operating days / yr | Claim | 300 | 320 | 340 | days |
Registry: gm [0.15, 0.2, 0.26] · opex [0.1, 0.13, 0.17] · DIO 42d · DSO 58d · DPO 42d · η 3
Occupancy — owned vs rented
| Rent/mo · metro | ₹60,000 / ₹1.20 L / ₹2.40 L |
| Rent/mo · non-metro | ₹22,000 / ₹50,000 / ₹95,000 |
| Deposit → BS asset | 6 months |
Owned signal: DISCOM bill + Udyam in firm name, no lease → owned shed; substitute plant/mould-tooling collateral note for rent
Balance-sheet build
Inventory = RM (granules, price-linked to crude) + WIP (on-machine + printing/lamination stages) + FG (finished packaging awaiting despatch); DIO worksheet overrides benchmark; receivables high 45–70d (B2B credit, buyer concentration); payables 30–50d to granule suppliers; fixed assets = injection/extrusion machines, moulds & dies (specialised, part-collateral), chillers, DG set
Guided capture — photo order
1 exterior 2 machinery 3 nameplate 4 storage 5 wip_floor 6 dispatch 7 utility_meter 8 production_register 9 gst_board 10 pukka_invoice 11 licence 12 udyam
Next-photo evidence · Eq 7
Injection/extruder shot/output counter Observed
Cycle counter × part weight (or extruder throughput) fixes kg/hr.
Monthly kWh vs connected load External
Injection/extrusion energy per kg fixes running utilisation (eta proxy).
B2B purchase order + tax invoice External
₹/kg conversion realisation by product.
Machine shift / job-card register Claim
Running hours per machine across shifts.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| E-way bills | granule inward & finished dispatch → converted tonnage and B2B buyer count | strong |
| GST GSTR-1/3B | declared turnover vs output × price; concentrated B2B buyers | strong |
| Electricity bill | connected load + kWh → machine utilisation and eta | strong |
| AA bank feed | granule purchases (large lumpy outflows) vs staggered B2B receipts → WC cycle | medium |
| Rental / Udyam | owned-vs-rented, deposit, capacity registration | medium |
Activity signals
| Footfall | n/a (B2B converter) |
| B2B / counterparties | Buyer GSTIN count on GSTR-1 (often a few large FMCG/industrial accounts → concentration risk) and granule-supplier GSTINs on inward e-way; dispatch register cartons/day and machine counters bound daily output |
Variance path: Energy proxy strong (injection/extrusion is power-dense): machine counter + kWh/kg close util/uph to ≤10%. Flag buyer concentration from GSTR-1 for receivables risk; e-way + GST concordance → ≤12%
Converts cotton/blended fibre into yarn on spindles and yarn into grey fabric on looms, running machines near-continuously across shifts; earns a value-addition margin per kg/metre where power and depreciation are the dominant costs.
Turnover · metro₹16.29 Cr
Turnover · non-metro₹10.22 Cr
EBITDA · metro₹97.75 L
Half-width (photo)±77%→34%
Revenue drivers · metro units/hr × productive hrs/day × utilisation × price/unit × operating days
| Driver | Class | lo | base | hi | |
|---|
| Yarn/fabric-equiv output / hr | Observed | 80 | 110 | 145 | kg/hr |
| Productive hrs / day | Claim | 18 | 22 | 23.5 | hr |
| Spindle/loom utilisation | Derived | 0.68 | 0.8 | 0.9 | fraction |
| Realisation / kg | Claim | 220 | 255 | 295 | ₹/kg |
| Operating days / yr | Claim | 310 | 330 | 350 | days |
Registry: gm [0.15, 0.19, 0.24] · opex [0.1, 0.13, 0.16] · DIO 55d · DSO 52d · DPO 40d · η 4.2
Occupancy — owned vs rented
| Rent/mo · metro | ₹1.20 L / ₹2.50 L / ₹5.00 L |
| Rent/mo · non-metro | ₹50,000 / ₹1.10 L / ₹2.20 L |
| Deposit → BS asset | 6 months |
Owned signal: HT connection + Udyam + property tax in firm name and no lease → owned mill; substitute plant-and-machinery/land collateral note for rent
Balance-sheet build
Inventory = RM (cotton/fibre bales, price-volatile) + WIP (bobbins/beams on machines, sizeable given long cycle) + FG (yarn cones/grey fabric); DIO worksheet overrides benchmark; receivables 45–60d (trade credit); payables 30–45d to fibre suppliers; fixed assets = spindles, looms, humidification, DG set, HT infra (major collateral)
Guided capture — photo order
1 exterior 2 machinery 3 nameplate 4 storage 5 rm_silo 6 wip_floor 7 dispatch 8 utility_meter 9 production_register 10 gst_board 11 pukka_invoice 12 licence 13 udyam
Next-photo evidence · Eq 7
Installed spindle/loom count + machine nameplate Observed
Spindle/loom count × rated speed sets the output ceiling.
HT power bill: contract demand + monthly kWh External
Power is the strongest run-rate signal; kWh vs connected load fixes utilisation.
3-shift attendance / production board Claim
Confirms continuous multi-shift running hours.
Yarn/fabric sale invoice sample External
Count/quality-wise ₹/kg realisation.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| Electricity/HT power bill | contract demand + kWh → machine run-rate, utilisation, eta (dominant signal) | strong |
| GST GSTR-1/3B | declared turnover vs output × price; yarn-count mix | strong |
| E-way bills | cotton/fibre inward & yarn/fabric outward → throughput and B2B counterparty count | strong |
| AA bank feed | receipts vs invoiced sales; power-bill autodebit corroborates load | medium |
| Udyam / factory licence | installed capacity band, worker count, registration | medium |
Activity signals
| Footfall | n/a (B2B mill) |
| B2B / counterparties | Distinct buyer GSTINs on GSTR-1 (traders/garment units) and fibre-supplier GSTINs on inward e-way bills; dispatch register bales/day bounds output and reconciles with power-derived run-rate |
Variance path: Capital-intensive and near-continuous run: power (kWh vs contract demand) is the decisive triangulation → util/uph gap closes to ≤10%; GST + e-way concordance strong. Photo-only ≈ ±20–25% until spindle count + power demand captured
Stocks a very deep SKU range of OEM and aftermarket parts, sells over the counter and on credit to garages/mechanics; earns on aftermarket margin, capital is tied up in slow-moving deep inventory and garage receivables.
Turnover · metro₹1.92 Cr
Turnover · non-metro₹88.70 L
EBITDA · metro₹21.15 L
Half-width (photo)±137%→15%
Revenue drivers · metro avg ticket × transactions/day × operating days
| Driver | Class | lo | base | hi | |
|---|
| Average ticket | Claim | 500 | 900 | 1600 | ₹ |
| Transactions / day | Observed | 30 | 60 | 100 | count |
| Operating days / yr | Claim | 345 | 356 | 362 | days |
Registry: gm [0.18, 0.23, 0.28] · opex [0.09, 0.12, 0.15] · DIO 110d · DSO 35d · DPO 42d · η 0.2
Occupancy — owned vs rented
| Rent/mo · metro | ₹25,000 / ₹55,000 / ₹1.10 L |
| Rent/mo · non-metro | ₹7,000 / ₹16,000 / ₹35,000 |
| Deposit → BS asset | 6 months |
Owned signal: electricity bill in proprietor's name + no rental agreement → owned shop/godown; substitute fixed-asset/collateral note (premises + racking) for rent
Balance-sheet build
inventory dominates BS = deep bin-rack SKU worksheet (Observed) overrides benchmark DIO, very high days-on-hand + dead-stock write-down risk; receivables material = garage credit 25–45d (DSO worksheet); payables = distributor credit 30–50d; fixed assets = racking, counters, delivery 2W
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 display 5 storage 6 price_board 7 qr_code 8 gst_board 9 utility_meter 10 pukka_invoice 11 credit_ledger 12 udyam
Next-photo evidence · Eq 7
Counter bill/challan sequence sample Observed
Bill/challan serials across a day fix transaction count including garage credit slips UPI misses.
Fast/slow part price sample Observed
Mix of low-value consumables vs high-value assemblies constrains blended ticket.
Garage/mechanic credit ledger Claim
Trade receivables khata bounds DSO and B2B share of sales.
Bin-rack deep-stock worksheet Observed
Racked SKU depth and dead-stock proportion set inventory value and days-on-hand.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| GST 3B/GSTR-1 + e-invoice | declared turnover & inbound part purchases (concordance) | strong |
| UPI/QR settlement | cash-counter banked turnover; credit sales excluded (residual via ledger) | medium |
| AA bank feed | garage receipts → receivables realisation & ageing | medium |
| Electricity bill | low load (retail/storage) → owned/rented; power cost minor | medium |
| Rental agreement | shop rent + deposit (BS) | medium |
Activity signals
| Footfall | 2 timed captures (morning garage-supply rush, afternoon) → footfall curve; cross-check vs bill sequence, not UPI (heavy credit) |
| B2B / counterparties | count garage/fleet counterparties from GSTR-1 + credit ledger; recurring accounts bound throughput |
Variance path: GST + bank concordance → ≤12%; UPI alone understates due to credit trade; ±25% photo-only until bill-sequence + garage ledger + bin worksheet close txns/dso/dio
Buys footwear by size/style matrix and sells at high retail mark-up to walk-ins; profit swings with season and end-of-season discount cycles, with size-curve and style-ageing inventory risk.
Turnover · metro₹1.50 Cr
Turnover · non-metro₹68.16 L
EBITDA · metro₹25.56 L
Half-width (photo)±148%→15%
Revenue drivers · metro avg ticket × transactions/day × operating days
| Driver | Class | lo | base | hi | |
|---|
| Average ticket | Claim | 600 | 1000 | 1800 | ₹ |
| Transactions / day | Observed | 22 | 42 | 75 | count |
| Operating days / yr | Claim | 350 | 358 | 363 | days |
Registry: gm [0.3, 0.37, 0.44] · opex [0.16, 0.2, 0.25] · DIO 82d · DSO 2d · DPO 40d · η 0.3
Occupancy — owned vs rented
| Rent/mo · metro | ₹45,000 / ₹95,000 / ₹2.00 L |
| Rent/mo · non-metro | ₹10,000 / ₹24,000 / ₹55,000 |
| Deposit → BS asset | 6 months |
Owned signal: electricity bill in proprietor's name + no rental agreement → owned; substitute fixed-asset/collateral note (frontage, fit-out) for rent
Balance-sheet build
inventory = wall + back-store box worksheet (Observed) overrides benchmark DIO, size-curve/style ageing → markdown risk; near-zero receivables (cash/UPI); payables = brand/distributor credit 30–45d; fixed assets = wall racks, seating, mirrors, POS, AC
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 price_board 5 display 6 storage 7 qr_code 8 gst_board 9 utility_meter 10 footfall_timed 11 pukka_invoice 12 udyam
Next-photo evidence · Eq 7
POS/Z-report day-total Observed
Bill count on a normal day constrains transactions; flag discount-week uplift separately.
Shoe-box MRP price sample Observed
Wall-display price range constrains blended ticket and net-of-discount realisation.
Wall + back-store box worksheet Observed
Box count by size/style sets inventory value and flags broken size-curve dead stock.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| UPI/QR + card settlement | banked retail turnover vs photo-derived; cash share = residual | strong |
| GST 3B/GSTR-1 | declared turnover vs photo-derived; seasonal filing pattern (concordance) | strong |
| Electricity bill | lighting/AC load → floor size sanity; owned/rented | medium |
| Rental agreement | rent expense + deposit (BS) — material opex line | strong |
Activity signals
| Footfall | 3 timed captures (weekday evening, weekend, sale week) → footfall curve with seasonality; cross-check vs POS + UPI count |
| B2B / counterparties | n/a — B2C retail |
Variance path: UPI+GST concordance → ≤10% on a normal month; ±25–30% photo-only until POS day-total + box price + stock worksheet close txns/ticket/dio; sale months need a discount-adjusted peak multiplier
Buys readymade apparel on seasonal cycles and sells at 30–45% mark-up to walk-in retail; profit swings with festival/wedding peaks and end-of-season discounting, with style/ageing inventory risk.
Turnover · metro₹1.93 Cr
Turnover · non-metro₹93.19 L
EBITDA · metro₹25.13 L
Half-width (photo)±149%→15%
Revenue drivers · metro avg ticket × transactions/day × operating days
| Driver | Class | lo | base | hi | |
|---|
| Average ticket | Claim | 700 | 1200 | 2200 | ₹ |
| Transactions / day | Observed | 25 | 45 | 80 | count |
| Operating days / yr | Claim | 350 | 358 | 363 | days |
Registry: gm [0.3, 0.36, 0.42] · opex [0.18, 0.23, 0.28] · DIO 85d · DSO 3d · DPO 40d · η 0.35
Occupancy — owned vs rented
| Rent/mo · metro | ₹50,000 / ₹1.10 L / ₹2.50 L |
| Rent/mo · non-metro | ₹12,000 / ₹28,000 / ₹60,000 |
| Deposit → BS asset | 6 months |
Owned signal: electricity bill in proprietor's name + no rental agreement → owned; substitute fixed-asset/collateral note (frontage, fit-out) for rent
Balance-sheet build
inventory = rack + back-stock unit worksheet (Observed) overrides benchmark DIO, style/season ageing → markdown risk on carried stock; near-zero receivables (cash/UPI); payables = supplier credit 30–45d; fixed assets = display fixtures, mannequins, trial rooms, AC, POS
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 price_board 5 display 6 storage 7 qr_code 8 gst_board 9 utility_meter 10 footfall_timed 11 pukka_invoice 12 udyam
Next-photo evidence · Eq 7
POS/Z-report day-total Observed
Bill count on a normal day constrains transactions; note festival multiplier separately.
Garment MRP tag sample Observed
Rack tag prices across segments constrain blended average ticket and discount depth.
Rack + back-stock unit worksheet Observed
Hanging + shelved unit count sets inventory value and flags ageing/off-season stock.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| UPI/QR + card settlement | banked retail turnover vs photo-derived; cash share = residual | strong |
| GST 3B/GSTR-1 | declared turnover vs photo-derived; seasonal filing pattern (concordance) | strong |
| Electricity bill | AC/lighting load → floor size sanity; owned/rented | medium |
| Rental agreement | rent expense + deposit (BS) — large opex line | strong |
Activity signals
| Footfall | 3 timed captures (weekday evening, weekend, festival week) → footfall curve capturing seasonality; cross-check vs POS + UPI count and trial-room turnover |
| B2B / counterparties | n/a — B2C retail; wholesale offtake rare |
Variance path: UPI+GST concordance → ≤10% on a normal month; ±25–30% photo-only until POS day-total + tag sample close txns/ticket; festival months need a separate peak multiplier, not a scaled base
Mixed counter-retail to walk-ins and credit trade sales to contractors/plumbers; earns on hardware and sanitaryware margin plus paint-tinting service, carries bulky slow-moving stock and contractor receivables.
Turnover · metro₹3.51 Cr
Turnover · non-metro₹1.69 Cr
EBITDA · metro₹24.60 L
Half-width (photo)±140%→17%
Revenue drivers · metro avg ticket × transactions/day × operating days
| Driver | Class | lo | base | hi | |
|---|
| Average ticket | Claim | 1200 | 2200 | 4000 | ₹ |
| Transactions / day | Observed | 25 | 45 | 75 | count |
| Operating days / yr | Claim | 345 | 355 | 362 | days |
Registry: gm [0.14, 0.17, 0.21] · opex [0.08, 0.1, 0.13] · DIO 65d · DSO 30d · DPO 35d · η 0.25
Occupancy — owned vs rented
| Rent/mo · metro | ₹30,000 / ₹60,000 / ₹1.20 L |
| Rent/mo · non-metro | ₹8,000 / ₹18,000 / ₹40,000 |
| Deposit → BS asset | 6 months |
Owned signal: electricity bill in proprietor's name + no rental agreement → owned shop/godown; substitute fixed-asset/collateral note (premises + racking) for rent
Balance-sheet build
inventory = bulky godown stock worksheet (Observed) overrides benchmark DIO, high value tied in slow-moving tiles/sanitaryware; receivables material = contractor credit 20–40d (DSO worksheet); payables = brand/distributor credit 25–40d; fixed assets = racking, tinting machine, delivery tempo, godown
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 price_board 5 display 6 storage 7 qr_code 8 gst_board 9 utility_meter 10 pukka_invoice 11 credit_ledger 12 udyam
Next-photo evidence · Eq 7
Counter bill/challan sequence sample Observed
Serial bill numbers across a day fix true transaction count including credit challans a UPI feed misses.
Paint/sanitaryware price-list sample Observed
High-value sanitaryware vs low-value hardware mix constrains blended average ticket.
Contractor credit ledger / khata Claim
Trade receivables book bounds DSO and B2B share vs cash counter sales.
Godown bulky-stock worksheet Observed
Pipe/tile/cement stacks and slow movers set inventory value and days-on-hand.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| GST 3B/GSTR-1 + e-way bills | declared turnover & bulky inbound movement (concordance) | strong |
| UPI/QR settlement | cash-counter banked turnover; credit sales excluded (residual via ledger) | medium |
| AA bank feed | contractor cheque/RTGS receipts → receivables realisation | medium |
| Electricity bill | connected load (tinting machine) → power cost; owned/rented | medium |
| Rental agreement | shop + godown rent + deposit (BS) | medium |
Activity signals
| Footfall | 2 timed captures (morning trade rush, evening retail) → footfall curve; cross-check vs bill sequence, not UPI (much sold on credit) |
| B2B / counterparties | count contractor counterparties from GSTR-1 + e-way bill consignees; credit ledger names bound B2B throughput |
Variance path: GST + e-way + bank concordance → ≤12%; UPI alone understates (credit trade); ±25% photo-only until bill-sequence + contractor ledger + godown worksheet close txns/dso/dio
Sells gold/silver ornaments where metal is near-pass-through and real income is making/wastage charges, hallmarking and old-gold exchange spread; very high ticket, low footfall, and enormous capital locked in metal inventory.
Turnover · metro₹12.82 Cr
Turnover · non-metro₹6.35 Cr
EBITDA · metro₹89.71 L
Half-width (photo)±167%→18%
Revenue drivers · metro avg ticket × transactions/day × operating days
| Driver | Class | lo | base | hi | |
|---|
| Average ticket | Claim | 25000 | 45000 | 80000 | ₹ |
| Transactions / day | Observed | 4 | 8 | 16 | count |
| Operating days / yr | Claim | 345 | 356 | 362 | days |
Registry: gm [0.09, 0.13, 0.18] · opex [0.04, 0.06, 0.08] · DIO 150d · DSO 6d · DPO 15d · η 0.15
Occupancy — owned vs rented
| Rent/mo · metro | ₹60,000 / ₹1.40 L / ₹3.00 L |
| Rent/mo · non-metro | ₹15,000 / ₹35,000 / ₹80,000 |
| Deposit → BS asset | 10 months |
Owned signal: electricity bill in proprietor's name + no rental agreement → owned (common for established jewellers); substitute fixed-asset/collateral note (premises, safe, gold stock) for rent
Balance-sheet build
inventory dominates the entire balance sheet = vault gold/silver weight worksheet (grams × karat × live rate, Observed) far overrides benchmark DIO; portion may be gold-loan financed (metal payable) — separate owned vs borrowed metal; receivables small (booking/scheme); scheme advances are a liability; fixed assets = safe/vault, CCTV, secured display, hallmark/assay kit
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 display 5 price_board 6 assay_kit 7 safe_vault 8 qr_code 9 gst_board 10 utility_meter 11 licence 12 udyam
Next-photo evidence · Eq 7
Making/wastage-charge bill sample Observed
Bill shows metal value + making charge split — constrains ticket and true gross margin (making charge, not metal, is income).
Hallmark/HUID sales register External
BIS HUID per sold piece gives an auditable ornament count/day that footfall cannot (very low txns).
Vault gold-stock weight worksheet Observed
Grams by karat × rate sets the dominant inventory value and days-on-hand — the balance-sheet driver.
Advance/monthly-scheme deposit book Claim
Customer scheme advances (a liability) and booking receivables adjust the trade cycle.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| GST 3B/GSTR-1 + e-invoice | declared turnover vs photo-derived; metal vs making split (concordance) | strong |
| BIS hallmark/HUID records | hallmarked-piece count → txns floor & authenticity | strong |
| UPI/card + AA bank feed | banked high-ticket receipts; scheme advances; cash share = residual | strong |
| Gold-loan / metal-account statement | metal borrowed vs owned → true inventory financing & collateral | medium |
| Rental agreement | rent expense + deposit (BS) | medium |
Activity signals
| Footfall | 2 timed captures (weekend, wedding/festival window) → footfall curve; cross-check vs HUID sales log, not walk-ins (browsing far exceeds buying) |
| B2B / counterparties | old-gold exchange and bullion counterparties in GSTR-1; wholesale/karigar job-work flows on challan |
Variance path: GST + HUID + bank concordance → ≤10%; photo-only ≈ ±35% because very low txns make count errors dominant and inventory value swings with gold rate; making-charge slip + HUID log + vault worksheet close ticket/txns/dio
Thin per-item markup on fast-moving staples (atta, oil, pulses, packaged FMCG) earned back through very high daily transaction frequency, mostly cash/UPI, on distributor credit.
Turnover · metro₹1.51 Cr
Turnover · non-metro₹68.64 L
EBITDA · metro₹7.54 L
Half-width (photo)±83%→15%
Revenue drivers · metro avg ticket × transactions/day × operating days
| Driver | Class | lo | base | hi | |
|---|
| Average basket | Claim | 180 | 250 | 340 | ₹ |
| Transactions / day | Observed | 110 | 170 | 260 | count |
| Operating days / yr | Claim | 345 | 355 | 362 | days |
Registry: gm [0.1, 0.13, 0.16] · opex [0.06, 0.08, 0.1] · DIO 26d · DSO 2d · DPO 20d · η 0.4
Occupancy — owned vs rented
| Rent/mo · metro | ₹25,000 / ₹45,000 / ₹80,000 |
| Rent/mo · non-metro | ₹6,000 / ₹12,000 / ₹22,000 |
| Deposit → BS asset | 6 months |
Owned signal: electricity bill in proprietor's name + no rental agreement in Mitra pack → owned; substitute a fixed-asset/collateral note (shop-cum-godown) for rent
Balance-sheet build
inventory = shelf + back-stock worksheet (Observed) overrides benchmark DIO; near-zero receivables (cash/UPI, small khata book); payables = distributor credit 15–30d; fixed assets = shelving, one/two refrigerators, weighing scale, POS/QR
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 display 5 price_board 6 qr_code 7 utility_meter 8 gst_board 9 udyam 10 rental_agreement 11 pukka_invoice 12 kacha_bill 13 footfall_timed
Next-photo evidence · Eq 7
POS / day-book Z-total Observed
Daily bill count pins transactions/day directly.
Shelf-price & basket photo Observed
Sampled shelf prices constrain the average basket.
Weekly-off / festival board Claim
Confirms weekly-off and closures → operating days.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| UPI/QR settlement | txns & ticket → banked turnover; cash share = residual | strong |
| GST 3B/GSTR-1 | declared turnover vs photo-derived (concordance) | strong |
| Electricity bill | connected load → floor/refrigeration sanity; owned vs rented; power cost | medium |
| Rental agreement | rent expense + deposit (BS) | medium |
Activity signals
| Footfall | 3 timed exterior/interior captures (morning re-stock lull, evening 6–9pm peak, weekend) → footfall curve; cross-check vs UPI txn count and Z-report |
| B2B / counterparties | n/a — pure B2C; occasional local tea-stall/tiffin re-seller is immaterial |
Variance path: UPI+GST concordance → ≤10%; photo-only ≈ ±25–30% until footfall curve + Z-report close the txns gap; cash-heavy tail is the main residual
Sells high-value handsets and electronics on thin metal-margin, earns real profit on accessories, extended warranty, activation and financier/EMI commissions; footfall low but ticket high.
Turnover · metro₹7.73 Cr
Turnover · non-metro₹3.83 Cr
EBITDA · metro₹23.20 L
Half-width (photo)±109%→16%
Revenue drivers · metro avg ticket × transactions/day × operating days
| Driver | Class | lo | base | hi | |
|---|
| Average ticket | Claim | 8000 | 12000 | 18000 | ₹ |
| Transactions / day | Observed | 10 | 18 | 30 | count |
| Operating days / yr | Claim | 350 | 358 | 363 | days |
Registry: gm [0.08, 0.11, 0.14] · opex [0.06, 0.08, 0.1] · DIO 38d · DSO 5d · DPO 24d · η 0.3
Occupancy — owned vs rented
| Rent/mo · metro | ₹40,000 / ₹90,000 / ₹1.80 L |
| Rent/mo · non-metro | ₹10,000 / ₹25,000 / ₹50,000 |
| Deposit → BS asset | 8 months |
Owned signal: electricity bill in proprietor's name + no rental agreement in Mitra pack → owned; substitute fixed-asset/collateral note (frontage, glass display) for rent
Balance-sheet build
inventory = serialised handset + accessory stock worksheet (Observed) overrides benchmark DIO, high unit value, obsolescence write-down risk on old models; receivables small (EMI settled by financier, some corporate credit); payables = distributor/brand credit 15–30d; fixed assets = display counters, security shutters, CCTV, POS/EMI terminal
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 price_board 5 display 6 storage 7 qr_code 8 gst_board 9 utility_meter 10 pukka_invoice 11 footfall_timed 12 udyam
Next-photo evidence · Eq 7
Financier/EMI activation register External
Financier console + activation slips fix true handset units/day (footfall alone undercounts high-ticket sales).
Sealed-box price / IMEI display Observed
Model mix on display constrains average ticket across handset tiers vs accessories.
GST purchase-register stock worksheet External
Serialised inbound e-invoices bound live inventory value and days-on-hand for fast-obsolescing SKUs.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| UPI/QR + card settlement | banked turnover vs photo-derived; EMI down-payments; cash share = residual | strong |
| GST 3B/GSTR-1 + e-invoice | declared turnover & serialised handset purchases (concordance) | strong |
| Financier/EMI statement | financed-unit count → txns floor; commission income | strong |
| Electricity bill | connected load → floor size & display power; owned/rented | medium |
| Rental agreement | rent expense + deposit (BS) | medium |
Activity signals
| Footfall | 3 timed exterior/interior captures (weekday evening, weekend afternoon, festival) → footfall curve; cross-check vs POS + EMI activation count, not walk-ins (many browse, few buy) |
| B2B / counterparties | minor B2B (bulk/corporate handset orders) visible in GSTR-1 counterparty list |
Variance path: UPI+card+EMI+GST concordance → ≤10%; photo-only ≈ ±30% because low-txn high-ticket means small count errors swing turnover; EMI register + stock worksheet close the txns/dio gap
Grows saplings, ornamentals and flowering plants on-site and sells them retail alongside pots, soil, seeds and garden services; own-production keeps COGS low so gross margin is high, but land, water, labour and plant mortality carry the cost, and demand is strongly seasonal.
Turnover · metro₹42.88 L
Turnover · non-metro₹17.06 L
EBITDA · metro₹8.58 L
Half-width (photo)±174%→22%
Revenue drivers · metro avg ticket × transactions/day × operating days
| Driver | Class | lo | base | hi | |
|---|
| Average sale | Claim | 180 | 320 | 600 | ₹ |
| Sales / day | Observed | 20 | 40 | 75 | count |
| Operating days / yr | Claim | 300 | 335 | 355 | days |
Registry: gm [0.38, 0.48, 0.58] · opex [0.22, 0.28, 0.35] · DIO 75d · DSO 10d · DPO 15d · η 0.3
Occupancy — owned vs rented
| Rent/mo · metro | ₹20,000 / ₹40,000 / ₹80,000 |
| Rent/mo · non-metro | ₹5,000 / ₹12,000 / ₹25,000 |
| Deposit → BS asset | 4 months |
Owned signal: land record / no lease deed + proprietor-name electricity (pump) bill → owned land; substitute a land fixed-asset/collateral note for rent — land is the principal security here
Balance-sheet build
inventory = living-plant worksheet by stage (seedling/growing/ready) with mortality haircut overrides benchmark DIO — long grow cycle inflates DIO; receivables = landscaping/institutional credit (DSO 10–20d); payables = seed/pot/fertiliser credit, short; fixed assets = land (or lease), polyhouse/shadenet, irrigation & pump, potting shed — land dominates collateral
Guided capture — photo order
1 exterior 2 neighbourhood 3 grow_area 4 display 5 storage 6 price_board 7 qr_code 8 utility_meter 9 gst_board 10 udyam 11 rental_agreement 12 pukka_invoice 13 footfall_timed
Next-photo evidence · Eq 7
Grow-area / polyhouse extent photo Observed
Bed/polyhouse area and plant count set saleable throughput → sales/day.
Plant & pot price-tag sample Observed
Spread from seedling to specimen plant constrains the average sale.
Season / event-order note Claim
Monsoon-planting & festival peaks vs summer lean set effective operating days.
Timed footfall / vehicle capture Observed
Weekend car footfall bounds walk-in sales/day.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| UPI/QR settlement | retail plant sales → banked turnover; event/landscaping jobs partly cash/cheque | medium |
| GST 3B/GSTR-1 (if registered) | declared turnover; many nurseries are unregistered (nursery produce partly exempt) → concordance weaker | medium |
| Electricity/water (pump) bill | irrigation load & area sanity; owned-vs-leased land | medium |
| Land record / lease deed | tenure, area, and collateral value of land | strong |
Activity signals
| Footfall | weekend and evening timed captures (plus car/parking counts, as buyers arrive by vehicle) → footfall curve; UPI covers retail, so footfall + grow-area carry event/bulk volume |
| B2B / counterparties | partial B2B — landscaping contracts, corporate/event floral supply, municipal plantation orders on credit; count institutional payers from GSTR-1 & bank credits → bounds the DSO tail and lumpier revenue |
Variance path: high margin but lumpy: retail UPI concordance ≤15%, but event/landscaping orders swing months widely; grow-area survey + season ledger are the tighteners; living-inventory valuation is the main BS judgement
Micro-margin, ultra-high-frequency counter sales of pan, cigarettes, gutka, snacks and cold drinks from a tiny footprint; almost entirely cash with a small UPI tail, negligible stock depth.
Turnover · metro₹31.50 L
Turnover · non-metro₹14.81 L
EBITDA · metro₹4.41 L
Half-width (photo)±117%→17%
Revenue drivers · metro avg ticket × transactions/day × operating days
| Driver | Class | lo | base | hi | |
|---|
| Average sale | Claim | 15 | 25 | 45 | ₹ |
| Sales / day | Observed | 220 | 350 | 520 | count |
| Operating days / yr | Claim | 350 | 360 | 365 | days |
Registry: gm [0.2, 0.28, 0.36] · opex [0.1, 0.14, 0.18] · DIO 14d · DSO 0d · DPO 6d · η 0.12
Occupancy — owned vs rented
| Rent/mo · metro | ₹5,000 / ₹12,000 / ₹25,000 |
| Rent/mo · non-metro | ₹1,500 / ₹3,500 / ₹7,000 |
| Deposit → BS asset | 3 months |
Owned signal: no rental agreement + own gumti/attached-to-shop → treat as owned/nil-rent; collateral negligible
Balance-sheet build
inventory = display + one shelf of cartons (DIO ~2 weeks on shelf-stable tobacco; betel leaf daily) worksheet overrides benchmark; nil receivables; payables = distributor credit ~1 week; fixed assets = kiosk, glass display, one fridge, hanging racks
Guided capture — photo order
1 exterior 2 neighbourhood 3 display 4 price_board 5 qr_code 6 utility_meter 7 udyam 8 rental_agreement 9 kacha_bill 10 footfall_timed
Next-photo evidence · Eq 7
Cigarette/pan-masala restock slip Observed
Packets/cartons restocked per week bound sticks/pouches sold → sales/day.
Counter rate-card photo Observed
Item price mix constrains the tiny average sale.
Timed footfall capture Observed
Peak-hour counter queue bounds transactions/day.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| UPI/QR settlement | small banked share; cash dominates and must be residual | weak |
| Restock / purchase slips | cigarette & pan-masala inflow → volume floor, gm sanity | strong |
| Electricity bill | tiny load (light + one fridge) → premises sanity | weak |
| Udyam | registration & vintage | medium |
Activity signals
| Footfall | 2–3 timed captures (office in/out, late-evening) → footfall curve; UPI badly under-counts so restock slips + footfall carry volume |
| B2B / counterparties | n/a — B2C counter only |
Variance path: highly cash so UPI concordance weak (≥±18%); restock-slip volume + footfall are the only reliable tighteners; regulatory note — tobacco sale, keep to observed stock not claims
Sells prescription and OTC medicines, and FMCG/wellness, at regulated margins under a drug licence; higher ticket and deep, expiry-managed inventory, cold-chain for biologics, and a slice of credit sales to regulars and institutions.
Turnover · metro₹1.89 Cr
Turnover · non-metro₹74.40 L
EBITDA · metro₹13.23 L
Half-width (photo)±97%→15%
Revenue drivers · metro avg ticket × transactions/day × operating days
| Driver | Class | lo | base | hi | |
|---|
| Average bill | Claim | 220 | 350 | 520 | ₹ |
| Bills / day | Observed | 90 | 150 | 230 | count |
| Operating days / yr | Claim | 350 | 360 | 364 | days |
Registry: gm [0.16, 0.2, 0.24] · opex [0.1, 0.13, 0.16] · DIO 62d · DSO 8d · DPO 35d · η 0.55
Occupancy — owned vs rented
| Rent/mo · metro | ₹30,000 / ₹55,000 / ₹95,000 |
| Rent/mo · non-metro | ₹9,000 / ₹16,000 / ₹30,000 |
| Deposit → BS asset | 6 months |
Owned signal: proprietor-name electricity bill + no rental agreement → owned; substitute fixed-asset/collateral note (shop + cold-chain kit) for rent
Balance-sheet build
inventory = shelf + rack + fridge worksheet with expiry ageing (returns to stockist) overrides DIO; receivables = institutional & khata credit (DSO 5–15d); payables = stockist credit 30–45d giving a favourable trade cycle; fixed assets = racking, refrigerator/cold-chain, billing PC, AC, CCTV
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 display 5 storage 6 cold_room 7 price_board 8 qr_code 9 utility_meter 10 gst_board 11 licence 12 udyam 13 rental_agreement 14 pukka_invoice 15 footfall_timed
Next-photo evidence · Eq 7
Billing-software Z-report Observed
Chemist billing software gives an exact daily bill count and value.
Sample bills / GST invoices Observed
Prescription bill values constrain the average ticket and margin mix.
Refrigerator / cold-chain photo Observed
Fridge depth (insulin/vaccine stock) proxies chronic-refill footfall → bills/day.
Timed footfall capture Observed
Post-OPD evening rush bounds transactions/day.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| UPI/QR + card settlement | txns & ticket → banked turnover; low cash share expected | strong |
| GST 3B/GSTR-1 | declared turnover vs photo-derived (concordance) | strong |
| Distributor invoices / e-way | drug purchases → COGS, regulated gm, DIO depth | strong |
| Drug licence (Form 20/21) + pharmacist reg. | legitimacy, scope, and continuity of operation | strong |
| Rental agreement | rent expense + deposit (BS) | medium |
Activity signals
| Footfall | timed captures near clinic/OPD close and evening → footfall curve; strong UPI/card coverage means POS reconciles footfall well |
| B2B / counterparties | partial B2B — supplies to nearby clinics/nursing homes on credit; count institutional payers from GSTR-1 & bank credits → bounds the DSO tail |
Variance path: billing software + UPI/GST concordance → ≤8–10%; main residuals are credit-sale timing (DSO) and near-expiry stock haircut, not turnover
Buys general merchandise (apparel, footwear, household, gifting) at trade discount and sells at MRP-linked markup; fewer but larger tickets than a kirana, slower stock turns, some seasonal peaks.
Turnover · metro₹1.57 Cr
Turnover · non-metro₹72.45 L
EBITDA · metro₹12.60 L
Half-width (photo)±100%→19%
Revenue drivers · metro avg ticket × transactions/day × operating days
| Driver | Class | lo | base | hi | |
|---|
| Average bill | Claim | 300 | 450 | 650 | ₹ |
| Bills / day | Observed | 60 | 100 | 160 | count |
| Operating days / yr | Claim | 330 | 350 | 360 | days |
Registry: gm [0.16, 0.2, 0.25] · opex [0.09, 0.12, 0.15] · DIO 48d · DSO 4d · DPO 28d · η 0.35
Occupancy — owned vs rented
| Rent/mo · metro | ₹30,000 / ₹55,000 / ₹1.00 L |
| Rent/mo · non-metro | ₹8,000 / ₹15,000 / ₹28,000 |
| Deposit → BS asset | 6 months |
Owned signal: proprietor-name electricity bill + no rental agreement → owned; substitute fixed-asset/collateral note (shop) for rent
Balance-sheet build
inventory = rack + back-stock worksheet with ageing (seasonal dead-stock haircut) overrides DIO; modest receivables (card float, small credit); payables = supplier credit 30–45d; fixed assets = fixtures, trial rooms, signage, POS, AC
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 display 5 price_board 6 storage 7 qr_code 8 utility_meter 9 gst_board 10 udyam 11 rental_agreement 12 pukka_invoice 13 footfall_timed
Next-photo evidence · Eq 7
POS Z-report / bill book Observed
Daily bill count pins transactions/day.
Price-tag & MRP sample Observed
Range of tagged prices constrains average bill and markup.
Timed footfall capture Observed
Weekend-vs-weekday footfall bounds conversion to bills.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| UPI/QR + card settlement | txns & ticket → banked turnover; cash share = residual | strong |
| GST 3B/GSTR-1 | declared turnover vs photo-derived (concordance) | strong |
| Purchase invoices / e-way | COGS & trade discount → gm sanity; stock inflow | medium |
| Rental agreement | rent expense + deposit (BS) | medium |
Activity signals
| Footfall | timed exterior captures (weekday evening, Saturday, Sunday) → footfall curve; conversion-to-bill ratio cross-checks POS/UPI count |
| B2B / counterparties | n/a — B2C; small institutional/bulk gifting orders visible in GSTR-1 if present |
Variance path: UPI+card+GST concordance → ≤10–12%; photo-only ≈ ±25% owing to ticket dispersion across categories; ageing worksheet is key for BS, not turnover
Blends low-margin stationery retail (books, paper, pens) with high-margin photocopy, print, lamination and DTP service income; strongly seasonal around exams and the school reopening cycle.
Turnover · metro₹33.66 L
Turnover · non-metro₹16.09 L
EBITDA · metro₹4.04 L
Half-width (photo)±133%→18%
Revenue drivers · metro avg ticket × transactions/day × operating days
| Driver | Class | lo | base | hi | |
|---|
| Average sale | Claim | 50 | 85 | 150 | ₹ |
| Transactions / day | Observed | 70 | 120 | 190 | count |
| Operating days / yr | Claim | 300 | 330 | 350 | days |
Registry: gm [0.24, 0.3, 0.38] · opex [0.14, 0.18, 0.23] · DIO 55d · DSO 5d · DPO 22d · η 1.1
Occupancy — owned vs rented
| Rent/mo · metro | ₹15,000 / ₹30,000 / ₹55,000 |
| Rent/mo · non-metro | ₹5,000 / ₹10,000 / ₹18,000 |
| Deposit → BS asset | 4 months |
Owned signal: proprietor-name electricity bill + no rental agreement → owned; substitute fixed-asset note (copiers + shop) for rent
Balance-sheet build
inventory = stationery shelves + paper/toner stock worksheet (seasonal build-up before school reopen) overrides DIO; small receivables (institutional print credit); payables = distributor credit 15–30d; fixed assets = photocopiers, printers, laminator, binding & DTP PC — high depreciation, consumables (toner/paper) a live opex line; elevated eta reflects copier power draw
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 display 5 machinery 6 price_board 7 qr_code 8 utility_meter 9 gst_board 10 udyam 11 rental_agreement 12 pukka_invoice 13 footfall_timed
Next-photo evidence · Eq 7
Photocopier page-counter reading Observed
Machine lifetime/period page count converts to copy jobs → service transactions/day.
Service & item rate board Observed
Copy/print/lamination rates plus stationery prices constrain average sale.
Exam / school-reopen calendar note Claim
Peak months vs lean months set effective operating-day weighting.
Timed footfall capture Observed
After-school / office-hour rush bounds transactions/day.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| UPI/QR settlement | service & retail txns → banked turnover; small-value cash residual | strong |
| Electricity bill | copier/printer load is high → service intensity proxy; owned vs rented | strong |
| GST 3B/GSTR-1 | declared turnover vs photo-derived (concordance) | medium |
| Rental agreement | rent expense + deposit (BS) | medium |
Activity signals
| Footfall | timed captures after school hours and around exam season → footfall curve; copier page-counter is the strongest single activity signal, cross-checked vs UPI |
| B2B / counterparties | partial B2B — bulk DTP/print & institutional copy jobs on credit; count recurring office/coaching payers from bank credits → bounds service revenue |
Variance path: copier counter + UPI concordance → ≤12%; seasonality is the big swing (exam/reopen months can be 2–3× lean months) so annualise on weighted days, not a flat month
Buys perishable produce daily at the mandi on cash and sells same-day at a volume markup; margin is real but eroded by spoilage and weight loss, so near-zero inventory days and very high transaction frequency at a tiny ticket.
Turnover · metro₹54.00 L
Turnover · non-metro₹28.33 L
EBITDA · metro₹4.86 L
Half-width (photo)±105%→17%
Revenue drivers · metro avg ticket × transactions/day × operating days
| Driver | Class | lo | base | hi | |
|---|
| Average sale | Claim | 35 | 60 | 95 | ₹ |
| Sales / day | Observed | 150 | 250 | 380 | count |
| Operating days / yr | Claim | 350 | 360 | 365 | days |
Registry: gm [0.16, 0.22, 0.28] · opex [0.09, 0.13, 0.17] · DIO 2d · DSO 0d · DPO 2d · η 0.15
Occupancy — owned vs rented
| Rent/mo · metro | ₹8,000 / ₹18,000 / ₹35,000 |
| Rent/mo · non-metro | ₹2,500 / ₹5,000 / ₹10,000 |
| Deposit → BS asset | 3 months |
Owned signal: no rental agreement + municipal hawking licence or own frontage → treat as owned/nil-rent; collateral is negligible (stock is perishable)
Balance-sheet build
inventory ≈ one day's stock only (perishable) → DIO near-zero; nil receivables; payables ≈ nil (mandi is cash) so no trade-cycle cushion; fixed assets = weighing scale, crates, thela/stall, tarpaulin — spoilage carried as an opex line, not inventory
Guided capture — photo order
1 exterior 2 neighbourhood 3 display 4 price_board 5 qr_code 6 utility_meter 7 weighbridge 8 udyam 9 rental_agreement 10 kacha_bill 11 footfall_timed
Next-photo evidence · Eq 7
Mandi purchase slip (kacha) Observed
Daily kg bought less spoilage bounds sellable volume → sales/day.
Chalkboard rate photo Observed
Per-kg rates × typical 0.5–1kg buy constrain average sale.
Timed footfall capture Observed
Morning and evening rush counts bound sales/day.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| UPI/QR settlement | banked share of sales; heavy cash residual expected | medium |
| Mandi purchase slips | daily COGS & volume → turnover floor; spoilage estimate | strong |
| Electricity bill | minimal load (lights/fan) → premises sanity; owned/rented | weak |
| Municipal hawking licence | legitimacy of pitch + fixed pitch fee | medium |
Activity signals
| Footfall | 2 timed captures (7–9am, 6–9pm) capture the twin produce rushes → footfall curve; UPI mostly under-counts, so footfall + mandi slip carry the volume estimate |
| B2B / counterparties | n/a — B2C walk-in; small supply to local eateries may show as recurring UPI payers |
Variance path: cash-dominant so UPI concordance weak (≥±15%); mandi purchase slip + footfall are the tightening levers; spoilage assumption (5–12%) is the main margin uncertainty
Buys mixed scrap (metal, paper, plastic) by weight from pickers/households mostly in cash and sells sorted material to larger recyclers on a thin per-kg spread; profit rides on weighed tonnage and the blended rate, and cash intensity is inherent.
Turnover · metro₹1.27 Cr
Turnover · non-metro₹65.10 L
EBITDA · metro₹5.07 L
Half-width (photo)±110%→23%
Revenue drivers · metro tonnage/day × blended ₹/kg × operating days
| Driver | Class | lo | base | hi | |
|---|
| Tonnage / day | Observed | 0.5 | 1.1 | 2 | t |
| Blended price | Benchmark | 26 | 36 | 46 | ₹/kg |
| Operating days / yr | Claim | 300 | 320 | 345 | days |
Registry: gm [0.06, 0.09, 0.13] · opex [0.03, 0.05, 0.07] · DIO 8d · DSO 12d · DPO 5d · η 0.6
Occupancy — owned vs rented
| Rent/mo · metro | ₹12,000 / ₹25,000 / ₹50,000 |
| Rent/mo · non-metro | ₹4,000 / ₹9,000 / ₹18,000 |
| Deposit → BS asset | 4 months |
Owned signal: electricity in dealer's name + no rental agreement → owned yard/shop; substitute a fixed-asset/collateral note for rent
Balance-sheet build
inventory low, fast-clearing (DIO ~8d) = measured pile worksheet; receivables = short recycler credit (DSO ~12d); payables tiny — pickers paid cash (DPO ~5d); CASH-HEAVY so reconcile bank sales vs cash buys carefully; fixed assets = platform scale/kanta, cutter/baler, small tempo
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 weighbridge 5 storage 6 price_board 7 machinery 8 gst_board 9 utility_meter 10 qr_code 11 kacha_bill 12 pukka_invoice
Next-photo evidence · Eq 7
Weighing-scale / kanta slips Observed
Removes the dominant tonnage spread — daily weighed intake.
Rate board + material-mix photo Observed
Material mix fixes the blended ₹/kg.
Onward sale invoice to recycler External
Realised sale rate cross-checks blended price + banked share.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| Onward-sale invoices (to recyclers) | outward sold weight × rate → turnover; realised blended price | strong |
| AA bank feed | banked receipts from recyclers vs cash purchases from pickers — cash intensity gap | strong |
| GST 3B/GSTR-1 | declared turnover vs weighed-throughput estimate (concordance) | medium |
| Electricity bill | cutter/baler load → processing sanity; owned/rented | medium |
Activity signals
| Footfall | timed exterior captures of picker/handcart drop-offs at peak morning hours give a soft intake curve |
| B2B / counterparties | buy-side is fragmented cash pickers (hard to enumerate); sell-side = a few recyclers from onward invoices + GSTR-1 → those bound sold tonnage |
Variance path: weighing slips + onward invoices + bank → ≤15% (cash buy-side keeps a residual gap); photo-only ≈ ±30–35% until weighbridge/kanta slips close the tonnage spread
Earns a professional fee (≈ 6–12% of project cost) on a pipeline of projects billed across milestones; billable-staff capacity caps throughput and receivables run long.
Turnover · metro₹1.10 Cr
Turnover · non-metro₹37.50 L
EBITDA · metro₹14.30 L
Half-width (photo)±158%→24%
Revenue drivers · metro clients(jobs)/day × avg fee × operating days
| Driver | Class | lo | base | hi | |
|---|
| Projects billed / month | Claim | 1 | 2 | 3.5 | projects |
| Avg fee / project (₹, ~6–12% of cost) | Claim | 300000 | 500000 | 900000 | ₹ |
| Billable months / yr | Benchmark | 10 | 11 | 12 | months |
Registry: gm [0.7, 0.78, 0.85] · opex [0.56, 0.65, 0.72] · DIO 1d · DSO 85d · DPO 25d · η 0.2
Occupancy — owned vs rented
| Rent/mo · metro | ₹40,000 / ₹90,000 / ₹1.80 L |
| Rent/mo · non-metro | ₹10,000 / ₹20,000 / ₹40,000 |
| Deposit → BS asset | 6 months |
Owned signal: DISCOM bill in principal's name + no rental agreement → owned studio; substitute a fixed-asset/collateral note (office + workstations) for rent
Balance-sheet build
near-zero inventory (DIO≈1); receivables run long (milestone billing, retention) → high DSO drives WCR; payables = outsourced structural/MEP consultants 20–30d; fixed assets = workstations, plotters, software licences, models
Guided capture — photo order
1 exterior 2 interior 3 staff_seating 4 portfolio_board 5 engagement_letter 6 fee_schedule 7 receivables_ageing 8 licence 9 utility_meter 10 gst_board 11 pukka_invoice 12 udyam
Next-photo evidence · Eq 7
Signed engagement letters / proposals External
Counts live projects and contracted fee; tightens throughput.
Fee schedule / percentage-of-cost slab Observed
Fixes avg fee per project against project-cost band.
Receivables ageing / invoice ledger External
Confirms billed value and long DSO for working-capital sizing.
Billable-staff seating count Observed
Headcount caps concurrent projects (capacity ceiling).
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| Account-Aggregator bank feed | milestone collections vs billed fee; smooths lumpy inflows | strong |
| GST 3B/GSTR-1 | declared professional receipts vs photo-derived; GSTR-1 counterparty count = client count | strong |
| Municipal/sanctioned-drawing submissions | independent project count (external register) | medium |
| Rental agreement | office rent + deposit (BS) | medium |
| Electricity bill | office load sanity; owned/rented | weak |
Activity signals
| Footfall | n/a — appointment-based studio, no walk-in footfall to curve |
| B2B / counterparties | estimate client count from GSTR-1 counterparties, engagement-letter file and sanctioned-drawing register; billable headcount bounds concurrent projects |
Variance path: engagement letters + AA collections + GSTR-1 counterparty count → ≤15%; photo-only ≈ ±30–35% because project count and fee are both lumpy
Sells stylist time on a fixed number of chairs — service tickets (cut/colour/facial) plus retail product upsell — through booked appointments and walk-ins.
Turnover · metro₹42.60 L
Turnover · non-metro₹14.56 L
EBITDA · metro₹5.96 L
Half-width (photo)±112%→16%
Revenue drivers · metro clients(jobs)/day × avg fee × operating days
| Driver | Class | lo | base | hi | |
|---|
| Clients served / day (chairs × turns) | Observed | 15 | 24 | 36 | count |
| Average service ticket | Claim | 300 | 500 | 850 | ₹ |
| Operating days / yr | Claim | 340 | 355 | 362 | days |
Registry: gm [0.5, 0.58, 0.66] · opex [0.38, 0.44, 0.5] · DIO 30d · DSO 2d · DPO 18d · η 1.3
Occupancy — owned vs rented
| Rent/mo · metro | ₹30,000 / ₹55,000 / ₹1.10 L |
| Rent/mo · non-metro | ₹8,000 / ₹15,000 / ₹30,000 |
| Deposit → BS asset | 6 months |
Owned signal: electricity bill in proprietor's name + no rental agreement in Mitra pack → owned; substitute a fixed-asset/collateral note for rent
Balance-sheet build
inventory = retail products + colour/consumable stock worksheet (Observed) overrides DIO; near-zero receivables (cash/UPI); payables = product distributor credit 15–30d; fixed assets = chairs, mirrors, geysers, AC, dryers, product cabinet
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 chair_station 5 price_board 6 display 7 appointment_register 8 qr_code 9 utility_meter 10 gst_board 11 pukka_invoice 12 udyam
Next-photo evidence · Eq 7
Chair/station photo (installed capacity) Observed
Fixes seating capacity, capping clients/day.
Appointment/booking register (7-day) Observed
Actual bookings + walk-ins tighten daily throughput.
Service menu / rate card Observed
Menu mix constrains average service ticket.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| UPI/QR settlement | clients & fee → banked service revenue; cash tips/walk-ins = residual | strong |
| GST 3B/GSTR-1 | declared service turnover vs photo-derived (concordance) | medium |
| Electricity bill | connected load (dryers/AC/geysers) → chair count sanity; owned/rented | medium |
| Rental agreement | rent expense + deposit (BS asset) | medium |
Activity signals
| Footfall | 3 timed exterior/interior captures (weekday evening, weekend peak, mid-morning) → chair-utilisation curve; cross-check vs UPI txn count |
| B2B / counterparties | minor — occasional bridal/event packages; otherwise B2C |
Variance path: chair count + appointment register + rate card drive half-width to ≤15%; photo-only ≈ ±25–30% until walk-in/appointment split is captured
Charges recurring compliance retainers plus one-time audit/tax/filing fees across a client book; revenue is seasonal (audit and tax peaks) and leverages articled/junior staff.
Turnover · metro₹61.60 L
Turnover · non-metro₹22.27 L
EBITDA · metro₹9.86 L
Half-width (photo)±161%→23%
Revenue drivers · metro clients(jobs)/day × avg fee × operating days
| Driver | Class | lo | base | hi | |
|---|
| Clients billed / month | Claim | 40 | 70 | 120 | clients |
| Avg fee / client-month (retainer + filing) | Claim | 4500 | 8000 | 15000 | ₹ |
| Effective billing months / yr | Benchmark | 10 | 11 | 12 | months |
Registry: gm [0.72, 0.8, 0.88] · opex [0.55, 0.64, 0.72] · DIO 0d · DSO 55d · DPO 20d · η 0.16
Occupancy — owned vs rented
| Rent/mo · metro | ₹30,000 / ₹65,000 / ₹1.30 L |
| Rent/mo · non-metro | ₹8,000 / ₹16,000 / ₹32,000 |
| Deposit → BS asset | 6 months |
Owned signal: DISCOM bill in proprietor/firm name + no rental agreement → owned office; substitute a fixed-asset/collateral note for rent
Balance-sheet build
zero inventory (DIO=0); receivables moderate (retainers prompt, audit/tax fees billed after work → 30–75d) → seasonal DSO; payables = subcontracted audits/DSC/portal fees 15–25d; fixed assets = computers, software, library, office fit-out; rent deposit is a BS asset
Guided capture — photo order
1 exterior 2 interior 3 staff_seating 4 engagement_letter 5 fee_schedule 6 licence 7 receivables_ageing 8 utility_meter 9 gst_board 10 pukka_invoice 11 udyam
Next-photo evidence · Eq 7
Client / filing register (portal login count) External
GST/ITR/ROC filing count on portal fixes active client book.
Fee schedule / engagement letters External
Fixes avg retainer + filing fee per client.
Articled/junior-staff seating count Observed
Staff leverage caps clients serviceable in peak season.
Receivables ageing schedule External
Confirms billed value and seasonal DSO.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| Account-Aggregator bank feed | retainer + fee collections vs billed; monthly retainer credits confirm recurring base | strong |
| GST 3B/GSTR-1 | declared professional receipts vs photo-derived | strong |
| GST/Income-tax e-filing portal | independent count of filings handled = client-book proxy | strong |
| Rental agreement | office rent + deposit (BS) | medium |
Activity signals
| Footfall | n/a — appointment/office based; seasonal peaks (Jul tax, Sep–Nov audit) shape the monthly band |
| B2B / counterparties | estimate client book from portal filing counts, GSTR-1 counterparties and engagement-letter file; staff headcount bounds peak-season throughput |
Variance path: portal filing count + AA retainer credits + fee schedule → ≤12%; photo-only ≈ ±25–30% and understates seasonal audit/tax spikes
Large batches enrolled into term/annual programmes across classrooms; revenue = enrolled students × average monthly-equivalent fee × session months, with faculty salaries the heaviest cost.
Turnover · metro₹1.65 Cr
Turnover · non-metro₹55.00 L
EBITDA · metro₹26.40 L
Half-width (photo)±156%→22%
Revenue drivers · metro clients(jobs)/day × avg fee × operating days
| Driver | Class | lo | base | hi | |
|---|
| Enrolled students (classrooms × batch size) | Claim | 150 | 300 | 550 | students |
| Avg monthly-equivalent fee / student | Claim | 3000 | 5000 | 8500 | ₹ |
| Programme months / yr | Benchmark | 10 | 11 | 12 | months |
Registry: gm [0.44, 0.54, 0.62] · opex [0.3, 0.38, 0.45] · DIO 3d · DSO 12d · DPO 8d · η 1.5
Occupancy — owned vs rented
| Rent/mo · metro | ₹80,000 / ₹1.80 L / ₹4.00 L |
| Rent/mo · non-metro | ₹25,000 / ₹55,000 / ₹1.20 L |
| Deposit → BS asset | 6 months |
Owned signal: commercial electricity bill in proprietor's name + no rental agreement → owned; substitute a fixed-asset/collateral note for rent
Balance-sheet build
near-zero inventory (study material only, DIO ~3d); instalment/term fees give moderate receivables (DSO ~10–12d) while advance term fees sit as DEFERRED INCOME (liability); payables small; fixed assets = classroom furniture, projectors, AC, servers/LMS
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 classroom 5 enrolment_register 6 batch_timetable 7 price_board 8 qr_code 9 utility_meter 10 gst_board 11 pukka_invoice 12 udyam
Next-photo evidence · Eq 7
Admission / enrolment register (term) Observed
Term admissions tighten enrolled-student count.
Classroom count & seating photo Observed
Classrooms × seats × shifts cap enrolment.
Course fee structure board / brochure Observed
Programme-wise fees constrain blended monthly-equivalent fee.
Faculty roster / timetable Claim
Faculty count × batches cross-checks batch throughput and cost base.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| AA bank feed | lump-sum term-fee receipts → admissions count; advance fees → deferred income | strong |
| GST 3B/GSTR-1 | declared coaching turnover vs students×fee estimate (concordance) | strong |
| UPI/QR settlement | instalment fee inflows → collection cadence | medium |
| Electricity bill | multi-classroom load (AC/projectors) → capacity sanity; owned/rented | medium |
Activity signals
| Footfall | 3 timed exterior captures at shift changeovers (morning/evening batches, weekend) → batch-occupancy curve; cross-check vs admission register |
| B2B / counterparties | school/college tie-ups & bulk programmes — estimate from bulk GSTR-1 invoices |
Variance path: admission register + fee structure + classroom capacity close half-width to ≤12%; photo-only ±30% because enrolment across shifts and instalment timing are unobservable from a single frame
Bills billable people at a rate across a mix of monthly retainers and fixed-fee projects; almost no inventory, but corporate clients pay slowly so receivables dominate the balance sheet.
Turnover · metro₹1.23 Cr
Turnover · non-metro₹38.50 L
EBITDA · metro₹14.78 L
Half-width (photo)±157%→24%
Revenue drivers · metro clients(jobs)/day × avg fee × operating days
| Driver | Class | lo | base | hi | |
|---|
| Active engagements / month | Claim | 4 | 8 | 14 | engagements |
| Avg monthly billing / engagement | Claim | 80000 | 140000 | 250000 | ₹ |
| Billable months / yr | Benchmark | 10 | 11 | 12 | months |
Registry: gm [0.72, 0.8, 0.88] · opex [0.58, 0.68, 0.76] · DIO 0d · DSO 80d · DPO 25d · η 0.15
Occupancy — owned vs rented
| Rent/mo · metro | ₹45,000 / ₹1.00 L / ₹2.00 L |
| Rent/mo · non-metro | ₹10,000 / ₹22,000 / ₹45,000 |
| Deposit → BS asset | 6 months |
Owned signal: DISCOM bill in firm/partner's name + no rental agreement → owned office; substitute a fixed-asset/collateral note for rent
Balance-sheet build
zero inventory (DIO=0); receivables are the dominant asset (30–90d corporate terms) → high DSO drives WCR; payables = subcontractors/associates 20–30d; fixed assets = laptops, software, office fit-out; deposits (rent) held as BS asset
Guided capture — photo order
1 exterior 2 interior 3 staff_seating 4 engagement_letter 5 fee_schedule 6 receivables_ageing 7 qr_code 8 utility_meter 9 gst_board 10 pukka_invoice 11 udyam
Next-photo evidence · Eq 7
Retainer agreements / SOWs on file External
Counts active engagements and contracted monthly value.
Rate card / MSA billing rates Observed
Fixes blended billing rate per engagement.
Timesheet / utilisation report Claim
Billable headcount × utilisation caps concurrent engagements.
Receivables ageing schedule External
Confirms billed value and long DSO for WCR.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| Account-Aggregator bank feed | retainer + project collections vs billed; steady retainer credits confirm recurring base | strong |
| GST 3B/GSTR-1 | declared service receipts vs photo-derived; GSTR-1 counterparty count = client count | strong |
| TDS 26AS / Form 16A | client-side TDS credits corroborate billed fees | medium |
| Rental agreement | office rent + deposit (BS) | medium |
Activity signals
| Footfall | n/a — B2B office, no walk-in footfall |
| B2B / counterparties | estimate client count from GSTR-1 counterparties, TDS deductor list and SOW file; billable headcount × utilisation bounds throughput |
Variance path: SOW file + AA retainer credits + GSTR-1 counterparty count → ≤12%; photo-only ≈ ±30% until engagement count and rate are fixed
Runs assays on samples — tests/day × average test price — driven by walk-ins plus doctor/hospital referrals; reagent-and-equipment heavy with NABL/quality overhead.
Turnover · metro₹84.48 L
Turnover · non-metro₹34.80 L
EBITDA · metro₹15.21 L
Half-width (photo)±181%→16%
Revenue drivers · metro clients(jobs)/day × avg fee × operating days
| Driver | Class | lo | base | hi | |
|---|
| Tests / day | Observed | 40 | 80 | 150 | tests |
| Average test price | Claim | 150 | 300 | 600 | ₹ |
| Operating days / yr | Claim | 340 | 352 | 362 | days |
Registry: gm [0.46, 0.56, 0.64] · opex [0.3, 0.38, 0.45] · DIO 30d · DSO 35d · DPO 35d · η 3.2
Occupancy — owned vs rented
| Rent/mo · metro | ₹35,000 / ₹70,000 / ₹1.50 L |
| Rent/mo · non-metro | ₹10,000 / ₹22,000 / ₹45,000 |
| Deposit → BS asset | 6 months |
Owned signal: commercial electricity bill in proprietor's name + no rental agreement → owned; substitute a fixed-asset/collateral note (analyzers + premises) for rent
Balance-sheet build
reagent/kit inventory + cold-chain consumables (DIO ~25–30d) via kit worksheet; HIGH receivables from doctor/hospital/TPA referrals & insurance (DSO ~28–35d) unlike cash-only services; reagent-supplier payables 30–35d; fixed assets = analyzers, centrifuges, refrigeration — equipment-heavy collateral
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 machinery 5 assay_kit 6 sample_collection 7 test_register 8 price_board 9 nabl_certificate 10 qr_code 11 utility_meter 12 gst_board 13 pukka_invoice 14 udyam
Next-photo evidence · Eq 7
Test register / LIS day-count Observed
Logged tests/day directly constrain throughput.
Analyzer/equipment photo (capacity & menu) Observed
Installed analyzers cap daily test capacity and test menu.
Test rate list / price board Observed
Test-mix rate card constrains average test price.
Reagent/kit purchase invoice Observed
Reagent cost per test bounds margin and cross-checks volume.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| GST 3B/GSTR-1 | declared diagnostic turnover vs tests×price estimate; B2B referral invoices (concordance) | strong |
| AA bank feed | TPA/insurance & hospital settlements → receivables ageing (DSO) | strong |
| e-way / reagent purchase invoices | reagent/kit inbound → test-volume floor & COGS | medium |
| Electricity bill | analyzer + cold-chain load → capacity sanity; owned/rented | medium |
Activity signals
| Footfall | 2 timed exterior captures at collection hours (morning fasting-sample peak, evening) → sample-intake curve; cross-check vs test register |
| B2B / counterparties | doctor/hospital referrals & TPA — estimate counterparty count from GSTR-1 B2B invoices; drives the higher DSO |
Variance path: test register + analyzer capacity + rate list close half-width to ≤12%; photo-only ±25–30% until B2B referral share and outsourced-test pass-through are pinned
Wins project-based events, quotes a lump-sum event value, takes 50–70% advance, and delivers via subcontracted labour and bought/rented material (flowers, fabric, lighting, catering pass-through); income is the retained margin over subcontract and consumable cost.
Turnover · metro₹1.65 Cr
Turnover · non-metro₹60.00 L
EBITDA · metro₹23.10 L
Half-width (photo)±212%→23%
Revenue drivers · metro clients(jobs)/day × avg fee × operating days
| Driver | Class | lo | base | hi | |
|---|
| Events / month | Claim | 3 | 6 | 10 | count |
| Avg event value | Claim | 80000 | 250000 | 600000 | ₹ |
| Active months / yr | Claim | 9 | 11 | 12 | months |
Registry: gm [0.3, 0.38, 0.46] · opex [0.18, 0.24, 0.3] · DIO 10d · DSO 25d · DPO 30d · η 0.15
Occupancy — owned vs rented
| Rent/mo · metro | ₹25,000 / ₹50,000 / ₹1.00 L |
| Rent/mo · non-metro | ₹8,000 / ₹16,000 / ₹32,000 |
| Deposit → BS asset | 5 months |
Owned signal: electricity bill in proprietor's name + owned godown, no rental agreement → owned; substitute fixed-asset/collateral note for rent
Balance-sheet build
owned reusable decor assets (lighting, drapes, props, structures) = fixed assets and collateral; consumables (flowers, disposables) small dio ~10d; receivables = balance-on-completion (dso ~25d) offset by 50–70% customer advances (liability, often negative net WC pre-event); payables = subcontracted labour & material vendors 25–30d
Guided capture — photo order
1 exterior 2 interior 3 storage 4 display 5 portfolio_album 6 qr_code 7 gst_board 8 utility_meter 9 rental_agreement 10 advance_ledger 11 pukka_invoice 12 udyam
Next-photo evidence · Eq 7
Signed event contracts (last 6) Observed
Fixes avg event value from actual quotes/orders.
Forward booking calendar Claim
Counts events/month incl. wedding-season peaks.
Advance-receipt ledger External
Confirms active months & advance-funded working capital.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| GST 3B/GSTR-1 | declared event turnover vs photo-derived events×value; input credits on subcontract/material | strong |
| Account-Aggregator bank feed | lumpy advance inflows + vendor/labour outflows → project cash cycle & margin | strong |
| UPI/QR settlement | advance & balance collections vs booking count | medium |
| Rental agreement | office/godown rent + deposit (BS) | medium |
| Electricity bill | godown load; owned/rented | weak |
Activity signals
| Footfall | n/a — no walk-in footfall; scene is godown/prop-stock condition, not counter traffic |
| B2B / counterparties | deal-count driven — count events from booking calendar + GSTR-1 B2B invoices (corporate/venue clients); average deal size × frequency bounds throughput; wedding/festival months carry 2–3× the off-season rate |
Variance path: signed contracts + AA advance ledger → ≤15%; photo-only ≈ ±35% because revenue is lumpy and project-based; strong wedding-season peak means a single-month capture over-states annual — require 12-mo calendar/contracts to smooth; abstain if only off-season captured
Recurring membership subscriptions — active members × average monthly fee — against a fixed floor and equipment set; low direct cost, high fixed rent and trainer payroll.
Turnover · metro₹57.50 L
Turnover · non-metro₹14.72 L
EBITDA · metro₹11.50 L
Half-width (photo)±120%→17%
Revenue drivers · metro clients(jobs)/day × avg fee × operating days
| Driver | Class | lo | base | hi | |
|---|
| Active members | Claim | 150 | 250 | 420 | members |
| Average monthly membership fee | Claim | 1400 | 2000 | 3200 | ₹ |
| Billing months / yr | Benchmark | 11 | 11.5 | 12 | months |
Registry: gm [0.75, 0.82, 0.88] · opex [0.55, 0.62, 0.7] · DIO 6d · DSO 2d · DPO 12d · η 2.6
Occupancy — owned vs rented
| Rent/mo · metro | ₹60,000 / ₹1.20 L / ₹2.50 L |
| Rent/mo · non-metro | ₹15,000 / ₹35,000 / ₹70,000 |
| Deposit → BS asset | 6 months |
Owned signal: electricity bill in proprietor's name + no rental agreement → owned; substitute a fixed-asset/collateral note (equipment + premises) for rent
Balance-sheet build
subscription model — near-zero inventory (only supplement stock); advance/annual fees create DEFERRED INCOME (liability) so DSO is low (2–5d) not high; payables minimal; fixed assets = cardio/strength equipment (major), AC, flooring, sound — equipment doubles as collateral
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 machinery 5 member_register 6 price_board 7 qr_code 8 utility_meter 9 gst_board 10 pukka_invoice 11 udyam
Next-photo evidence · Eq 7
Active-member register / app dashboard Observed
Active (non-lapsed) member count is the core driver.
Membership plan/rate board (monthly/quarterly/annual) Observed
Plan mix constrains blended monthly fee.
Equipment-floor photo (station count & area) Observed
Floor + equipment cap sustainable active membership.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| UPI/QR settlement | recurring fee inflows → active-member count & renewal cadence | strong |
| AA bank feed | monthly subscription pattern; advance (quarterly/annual) receipts → deferred income | strong |
| GST 3B/GSTR-1 | declared subscription turnover vs member×fee estimate | medium |
| Electricity bill | high connected load (AC + machines) → floor-size sanity; owned/rented | medium |
Activity signals
| Footfall | 3 timed exterior captures (early-morning peak, evening peak, weekend) → check-in curve; cross-check vs member register & UPI renewals |
| B2B / counterparties | corporate wellness tie-ups — estimate from bulk-invoice count in GSTR-1 |
Variance path: member register + fee-plan board close half-width to ≤12%; photo-only wide (±30%) because active-vs-lapsed membership is unobservable from the floor alone
Sells professional time/expertise as billable engagements at a per-client fee from a modest office; income is fee less low direct cost and office overhead — used as a catch-all when no specific profile fits.
Turnover · metro₹16.50 L
Turnover · non-metro₹6.93 L
EBITDA · metro₹3.30 L
Half-width (photo)±433%→25%
Revenue drivers · metro clients(jobs)/day × avg fee × operating days
| Driver | Class | lo | base | hi | |
|---|
| Clients / month | Claim | 8 | 25 | 60 | count |
| Avg fee / engagement | Claim | 1500 | 6000 | 20000 | ₹ |
| Active months / yr | Claim | 10 | 11 | 12 | months |
Registry: gm [0.55, 0.7, 0.85] · opex [0.35, 0.5, 0.65] · DIO 0d · DSO 45d · DPO 8d · η 0.06
Occupancy — owned vs rented
| Rent/mo · metro | ₹20,000 / ₹45,000 / ₹90,000 |
| Rent/mo · non-metro | ₹6,000 / ₹14,000 / ₹28,000 |
| Deposit → BS asset | 6 months |
Owned signal: electricity bill in proprietor's name + no rental agreement → owned/home-office; substitute fixed-asset/collateral note for rent
Balance-sheet build
no inventory (dio 0); receivables material (dso ~45d — professionals invoice and wait); payables minimal; fixed assets = office fit-out, computers, books/software; goodwill/personal-brand not on BS but supports durability note
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 price_board 5 appointment_register 6 qr_code 7 gst_board 8 utility_meter 9 rental_agreement 10 pukka_invoice 11 licence 12 udyam
Next-photo evidence · Eq 7
Appointment/client register (3 mo) Claim
Bounds clients/month; wide fallback prior needs this first.
Issued fee invoices sample Observed
Fixes avg engagement fee across service types.
Active-months + 26AS/GST cross-check External
Bounds active months/yr; corroborate with receipts.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| Account-Aggregator bank feed | professional-receipt credits → fee realisation & client frequency (primary anchor for a thin catch-all) | strong |
| GST 3B/GSTR-1 or 26AS/TDS | declared professional turnover vs photo-derived clients×fee (concordance) | strong |
| UPI/QR settlement | small-fee collections vs client count; cash residual | medium |
| Rental agreement | office rent + deposit (BS) | medium |
| Professional licence/registration | vintage, credential → income durability | medium |
Activity signals
| Footfall | low-frequency, high-value visits; appointment register + 2 timed captures preferred over exterior footfall |
| B2B / counterparties | mixed B2C/B2B; where clients are firms, count counterparties from GSTR-1/TDS to bound engagement volume |
Variance path: CATCH-ALL fallback — deliberately WIDE driver intervals; photo-only easily ±40–50%. Needs AA receipts + appointment register + invoice sample together to reach ≤20%; if capture is thin (no register, no bank/GST corroboration), ABSTAIN rather than emit a point estimate — assign to a specific profile if one fits before using this
Performs small paid jobs/services at a per-job charge with minimal premises and mostly cash collection; income is charge less basic material/consumable and labour — the most conservative catch-all when nothing else fits.
Turnover · metro₹14.40 L
Turnover · non-metro₹5.80 L
EBITDA · metro₹1.44 L
Half-width (photo)±529%→29%
Revenue drivers · metro clients(jobs)/day × avg fee × operating days
| Driver | Class | lo | base | hi | |
|---|
| Jobs / day | Claim | 3 | 12 | 30 | count |
| Charge / job | Claim | 100 | 400 | 1500 | ₹ |
| Operating days / yr | Claim | 260 | 300 | 340 | days |
Registry: gm [0.4, 0.55, 0.72] · opex [0.3, 0.45, 0.62] · DIO 3d · DSO 3d · DPO 5d · η 0.12
Occupancy — owned vs rented
| Rent/mo · metro | ₹8,000 / ₹20,000 / ₹45,000 |
| Rent/mo · non-metro | ₹3,000 / ₹8,000 / ₹18,000 |
| Deposit → BS asset | 3 months |
Owned signal: no rental agreement + electricity bill in own name (or no dedicated meter for roadside) → owned/home-based; substitute fixed-asset/collateral note, or flag premises informality
Balance-sheet build
minimal owned inventory (dio ~3d consumables); near-zero receivables (cash on completion); negligible payables; fixed assets = basic tools/equipment & premises fit-out (thin, low collateral value); high informality → treat GST/bank gaps as Gap nodes, not zeros
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 price_board 5 display 6 qr_code 7 utility_meter 8 rental_agreement 9 kacha_bill 10 udyam
Next-photo evidence · Eq 7
Cash day-book / job tally (2 wk) Claim
Very wide prior — counts jobs/day; corroborate with UPI.
Charge/rate sample per job Observed
Fixes charge per job across the service mix.
30-day UPI settlement (count + active days) External
Bounds operating days & seasonality; anchors cash residual.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| UPI/QR settlement | job count & charge → banked collections (primary anchor); large cash residual expected | strong |
| Electricity bill | connected load/premises existence; owned/rented; often thin | medium |
| Udyam | existence & self-declared activity/vintage | weak |
| Rental agreement | rent + deposit if formal premises (often absent) | weak |
Activity signals
| Footfall | 3+ timed exterior captures (weekday, weekend, peak hour) → job-arrival curve; cross-check vs UPI count; expect high cash share the curve alone can't bank |
| B2B / counterparties | predominantly B2C cash; rare institutional jobs counted from any kacha-bill/challan stub if present |
Variance path: MOST CONSERVATIVE catch-all — VERY WIDE driver intervals (jobs/day and charge span ~10x). Photo-only ±50%+ and documentary layer is usually thin. Requires UPI count + cash day-book + charge sample to approach ≤20%, and often cannot due to cash informality. HIGH ABSTENTION TENDENCY: emit an abstain/needs-more-evidence flag whenever UPI/bank corroboration is missing or the activity curve conflicts with the day-book; prefer a specific profile whenever one plausibly fits
Earns commission = policies sold × avg premium × commission rate, plus a renewal-trail income on the existing book; near-zero COGS/inventory, revenue lands as insurer payouts into the bank.
Turnover · metro₹34.65 L
Turnover · non-metro₹12.10 L
EBITDA · metro₹7.62 L
Half-width (photo)±241%→23%
Revenue drivers · metro clients(jobs)/day × avg fee × operating days
| Driver | Class | lo | base | hi | |
|---|
| Policies issued / month | Claim | 40 | 90 | 180 | policies |
| Avg commission / policy (premium × rate) | Claim | 1500 | 3500 | 8000 | ₹ |
| Active months / yr | Benchmark | 10 | 11 | 12 | months |
Registry: gm [0.82, 0.9, 0.96] · opex [0.58, 0.68, 0.78] · DIO 0d · DSO 35d · DPO 15d · η 0.14
Occupancy — owned vs rented
| Rent/mo · metro | ₹25,000 / ₹55,000 / ₹1.10 L |
| Rent/mo · non-metro | ₹6,000 / ₹14,000 / ₹28,000 |
| Deposit → BS asset | 6 months |
Owned signal: DISCOM bill in agent's name + no rental agreement → owned office; substitute a fixed-asset/collateral note for rent
Balance-sheet build
zero inventory (DIO=0); receivables = commission accrued but not yet paid by insurer (15–45d) → DSO; payables = sub-agent commission payouts 10–20d; renewal trail is an off-balance recurring-income annuity noted for cash-flow stability; fixed assets minimal (office fit-out, computers)
Guided capture — photo order
1 exterior 2 interior 3 staff_seating 4 policy_register 5 commission_statement 6 fee_schedule 7 renewal_register 8 licence 9 qr_code 10 utility_meter 11 gst_board 12 udyam
Next-photo evidence · Eq 7
Policy issuance register / portal count External
Counts policies issued/month from insurer portal or register.
Insurer commission statement External
Fixes avg commission per policy and confirms banked income.
Renewal-book / trail register External
Sizes recurring renewal-trail income (persistency).
AA bank inflows tagged to insurers External
Insurer credits reconcile total commission income independently.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| Account-Aggregator bank feed | insurer commission credits → total income; the primary ground truth for a cashless-inventory model | strong |
| Insurer commission statements | commission per policy, rate mix and renewal trail | strong |
| GST 3B/GSTR-1 | commission is a taxable service; declared vs bank-derived income | strong |
| IRDAI agency licence / portal | authorised lines and active-agent status | medium |
| Rental agreement | office rent + deposit (BS) | weak |
Activity signals
| Footfall | n/a — advisory/commission model, no product footfall |
| B2B / counterparties | estimate volume from insurer-portal policy counts, commission statements and AA insurer-tagged inflows; sub-agent headcount indicates sourcing capacity |
Variance path: AA insurer credits + commission statements → ≤10% (income is fully banked); photo-only ≈ ±30–40% since there is no physical stock/footfall to observe
Earns a design fee plus a margin on materials and execution across turnkey projects; collects advances up front but carries project work-in-progress and material stock until handover.
Turnover · metro₹1.32 Cr
Turnover · non-metro₹48.00 L
EBITDA · metro₹15.84 L
Half-width (photo)±233%→25%
Revenue drivers · metro clients(jobs)/day × avg fee × operating days
| Driver | Class | lo | base | hi | |
|---|
| Projects handed over / month | Claim | 0.8 | 1.5 | 3 | projects |
| Avg project value (fee + material margin) | Claim | 400000 | 800000 | 1800000 | ₹ |
| Active months / yr | Benchmark | 10 | 11 | 12 | months |
Registry: gm [0.3, 0.38, 0.46] · opex [0.2, 0.26, 0.32] · DIO 30d · DSO 40d · DPO 30d · η 0.25
Occupancy — owned vs rented
| Rent/mo · metro | ₹40,000 / ₹85,000 / ₹1.70 L |
| Rent/mo · non-metro | ₹10,000 / ₹20,000 / ₹42,000 |
| Deposit → BS asset | 6 months |
Owned signal: DISCOM bill in owner's name + no rental agreement → owned studio; substitute a fixed-asset/collateral note (studio + workshop) for rent
Balance-sheet build
inventory = material stock + project WIP worksheet (Observed) overrides benchmark DIO; receivables moderate (advances collected up front, retention/final at handover) → net DSO; payables = material vendors + contractors 25–40d; fixed assets = studio fit-out, samples, tools; customer advances sit as a BS liability offsetting WCR
Guided capture — photo order
1 exterior 2 interior 3 portfolio_board 4 project_wip 5 engagement_letter 6 fee_schedule 7 storage 8 pukka_invoice 9 qr_code 10 utility_meter 11 gst_board 12 udyam
Next-photo evidence · Eq 7
Signed project contracts + advance receipts External
Counts live projects and contracted value; advances confirm pipeline.
BOQ / rate card / fee schedule Observed
Fixes avg project value split (design fee vs material margin).
Material purchase invoices External
Sizes material margin (gm) and payables to vendors.
Project WIP / site-works register Observed
Confirms in-progress projects and WIP days for DIO.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| Account-Aggregator bank feed | advance + milestone inflows vs contracted value; advances net down receivables | strong |
| GST 3B/GSTR-1 | declared turnover (works-contract/service) vs photo-derived; input-credit on materials confirms COGS | strong |
| Material purchase invoices / e-way bills | material COGS, margin and DIO | medium |
| Rental agreement | studio rent + deposit (BS) | medium |
Activity signals
| Footfall | n/a — project-based; portfolio board and site photos evidence recent completions |
| B2B / counterparties | estimate active projects from contract file, e-way bills to site addresses and GSTR-1 counterparties; WIP register bounds concurrent execution |
Variance path: contracts + advance receipts + material invoices → ≤15%; photo-only ≈ ±30–35% because project value (fee vs material split) is lumpy
A small counter turns over repair jobs — jobs/day × average repair value (parts + labour) — with a warranty/out-of-warranty mix; labour is high-margin, parts pass-through.
Turnover · metro₹36.23 L
Turnover · non-metro₹15.30 L
EBITDA · metro₹6.52 L
Half-width (photo)±171%→17%
Revenue drivers · metro clients(jobs)/day × avg fee × operating days
| Driver | Class | lo | base | hi | |
|---|
| Jobs / day | Observed | 8 | 15 | 25 | jobs |
| Average repair value (parts + labour) | Claim | 400 | 700 | 1500 | ₹ |
| Operating days / yr | Claim | 330 | 345 | 358 | days |
Registry: gm [0.4, 0.48, 0.56] · opex [0.24, 0.3, 0.36] · DIO 25d · DSO 4d · DPO 15d · η 1.2
Occupancy — owned vs rented
| Rent/mo · metro | ₹15,000 / ₹30,000 / ₹60,000 |
| Rent/mo · non-metro | ₹5,000 / ₹10,000 / ₹22,000 |
| Deposit → BS asset | 5 months |
Owned signal: electricity bill in proprietor's name + no rental agreement → owned; substitute a fixed-asset/collateral note for rent
Balance-sheet build
modest spare-parts + accessory inventory (DIO ~20–25d); mostly cash/UPI so low receivables except warranty-claim reimbursements (DSO ~3–4d, longer for ASC claims); parts-supplier payables 12–15d; fixed assets = soldering/testing benches, tools, display counter
Guided capture — photo order
1 exterior 2 neighbourhood 3 repair_counter 4 display 5 storage 6 job_card_register 7 price_board 8 qr_code 9 utility_meter 10 gst_board 11 pukka_invoice 12 udyam
Next-photo evidence · Eq 7
Repair job-ticket register (7-day) Observed
Intake tickets tighten daily job count.
Counter + accessory display photo Observed
Accessory/retail mix lifts and constrains average job value.
Spare-part purchase invoice sample Observed
Part cost vs charge fixes parts/labour split within repair value.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| UPI/QR settlement | repair payments → banked turnover; cash jobs = residual | strong |
| GST 3B/GSTR-1 | declared turnover vs jobs×fee estimate (concordance) | medium |
| e-way / parts purchase invoices | spare-parts inbound → parts COGS & inventory | medium |
| Brand ASC agreement | authorised-service-centre warranty jobs → B2B receivable stream | medium |
Activity signals
| Footfall | 2 timed exterior captures (evening peak, weekend) → walk-in count; cross-check vs job-ticket register & UPI |
| B2B / counterparties | brand/ASC warranty reimbursements — estimate from claim invoices / GSTR-1 B2B lines |
Variance path: job-ticket register + parts-invoice split close half-width to ≤15%; photo-only ±25–30% until warranty-vs-paid mix and cash-job share are captured
Earns brokerage = deals closed × avg deal value × brokerage % (≈1–2% sale, ~1 month rent); income is lumpy and high-variance, landing as large irregular payments at closing.
Turnover · metro₹69.30 L
Turnover · non-metro₹19.25 L
EBITDA · metro₹15.25 L
Half-width (photo)±264%→27%
Revenue drivers · metro clients(jobs)/day × avg fee × operating days
| Driver | Class | lo | base | hi | |
|---|
| Deals closed / month | Claim | 1.5 | 3.5 | 7 | deals |
| Avg brokerage / deal (value × %) | Claim | 80000 | 180000 | 450000 | ₹ |
| Active months / yr | Benchmark | 10 | 11 | 12 | months |
Registry: gm [0.82, 0.9, 0.96] · opex [0.55, 0.68, 0.8] · DIO 0d · DSO 25d · DPO 10d · η 0.14
Occupancy — owned vs rented
| Rent/mo · metro | ₹30,000 / ₹60,000 / ₹1.20 L |
| Rent/mo · non-metro | ₹7,000 / ₹15,000 / ₹30,000 |
| Deposit → BS asset | 6 months |
Owned signal: DISCOM bill in broker's name + no rental agreement → owned office; substitute a fixed-asset/collateral note for rent
Balance-sheet build
zero inventory (DIO=0); receivables short (brokerage usually collected at/near closing, occasional 30d tail) → low DSO; payables = sub-broker splits 5–15d; income highly seasonal/lumpy so use a trailing-12-month average, not a spot month; fixed assets minimal (office, vehicles for site visits)
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 staff_seating 5 deal_register 6 commission_statement 7 fee_schedule 8 licence 9 qr_code 10 utility_meter 11 gst_board 12 udyam
Next-photo evidence · Eq 7
Deal register / agreement-to-sell records External
Counts closed deals/month; anchors the highest-variance driver.
Brokerage agreement / commission slab Observed
Fixes brokerage % and avg deal value band.
AA bank inflows (large irregular credits) External
Closing-brokerage credits reconcile realised income and smooth lumpiness.
Active-listing / mandate board Observed
Live mandates proxy pipeline that converts to deals.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| Account-Aggregator bank feed | large irregular brokerage credits → realised income; the key smoother for a lumpy model | strong |
| GST 3B/GSTR-1 | brokerage is a taxable service; declared vs bank-derived income | strong |
| RERA agent portal | registered-agent status and (where filed) transaction linkage | medium |
| Rental agreement | office rent + deposit (BS) | weak |
Activity signals
| Footfall | n/a — deal-driven, not footfall-driven; walk-in enquiries are weak signal |
| B2B / counterparties | estimate deal flow from deal register, RERA linkage, GSTR-1 counterparties and AA large-credit clustering; sub-broker headcount indicates coverage |
Variance path: deal register + AA large-credit reconciliation + GST → ≤15% on a trailing-12m basis; any single-month or photo-only read is ±40–60% due to deal lumpiness
Doctor sells time as consultations (consults/day × fee) with a thin pharmacy/procedure/injectables add-on layered on top; revenue is mostly cash/UPI at point of care.
Turnover · metro₹66.00 L
Turnover · non-metro₹40.00 L
EBITDA · metro₹11.88 L
Half-width (photo)±92%→17%
Revenue drivers · metro clients(jobs)/day × avg fee × operating days
| Driver | Class | lo | base | hi | |
|---|
| Consults / day | Observed | 25 | 40 | 55 | patients |
| Avg consultation + add-on fee | Claim | 350 | 550 | 850 | ₹ |
| Operating days / yr | Claim | 285 | 300 | 312 | days |
Registry: gm [0.55, 0.62, 0.68] · opex [0.38, 0.44, 0.5] · DIO 12d · DSO 8d · DPO 22d · η 0.35
Occupancy — owned vs rented
| Rent/mo · metro | ₹35,000 / ₹70,000 / ₹1.40 L |
| Rent/mo · non-metro | ₹8,000 / ₹16,000 / ₹32,000 |
| Deposit → BS asset | 6 months |
Owned signal: DISCOM bill in doctor's name + no rental agreement in Mitra pack → owned premises; substitute a fixed-asset/collateral note (clinic + equipment) for rent
Balance-sheet build
inventory = small drug/consumables stock worksheet (Observed) overrides benchmark DIO; low receivables (mostly cash/UPI; TPA/insurance panel adds a short tail → DSO); payables = pharma distributor credit 15–30d; fixed assets = diagnostic/procedure equipment, furniture, refrigeration for vaccines
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 licence 5 appointment_register 6 fee_schedule 7 display 8 storage 9 qr_code 10 gst_board 11 utility_meter 12 pukka_invoice 13 udyam
Next-photo evidence · Eq 7
Appointment/OP register day-count Observed
Counts patients seen/day directly; tightens the widest driver.
Consultation fee board / rate card Observed
Fixes base consultation fee; add-on inferred from pharmacy invoices.
UPI/QR settlement day-total External
Banked collections cross-check consult count × fee; cash share = residual.
Pharmacy/consumables purchase bill External
Sizes the dispensing add-on and pharmacy DIO.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| UPI/QR settlement | consult count × fee → banked turnover; cash consults = residual | strong |
| GST 3B/GSTR-1 | declared receipts (pharmacy/procedures are taxable; pure consultation often exempt) vs photo-derived | medium |
| Pharmacy/distributor invoices | add-on COGS and dispensing DIO | medium |
| Electricity bill | connected load → equipment (lights, AC, steriliser) sanity; owned/rented | medium |
| Rental agreement | rent expense + deposit (BS) | medium |
Activity signals
| Footfall | 3 timed exterior captures (morning OP, evening OP, weekend) → patient-arrival curve; cross-check vs appointment register and UPI txn count |
| B2B / counterparties | n/a — B2C; TPA/insurance panel receipts (if empanelled) explain the small receivables tail |
Variance path: appointment register + UPI + fee board → ≤12%; photo-only ≈ ±25–30% until patient day-count and fee are pinned
Charges a per-garment stitching/alteration fee on customer-supplied fabric; income is labour value less thread/consumables and helper wages, held-fabric belongs to customers and is not owned inventory.
Turnover · metro₹21.00 L
Turnover · non-metro₹8.26 L
EBITDA · metro₹4.41 L
Half-width (photo)±245%→18%
Revenue drivers · metro clients(jobs)/day × avg fee × operating days
| Driver | Class | lo | base | hi | |
|---|
| Garments / day | Observed | 5 | 10 | 18 | count |
| Stitching charge / garment | Claim | 300 | 700 | 1800 | ₹ |
| Operating days / yr | Claim | 280 | 300 | 330 | days |
Registry: gm [0.55, 0.65, 0.75] · opex [0.35, 0.44, 0.54] · DIO 3d · DSO 4d · DPO 5d · η 0.25
Occupancy — owned vs rented
| Rent/mo · metro | ₹12,000 / ₹25,000 / ₹50,000 |
| Rent/mo · non-metro | ₹4,000 / ₹9,000 / ₹18,000 |
| Deposit → BS asset | 4 months |
Owned signal: electricity bill in proprietor's name + no rental agreement → owned/home-based; substitute fixed-asset/collateral note for rent
Balance-sheet build
customer fabric held is NOT owned inventory (exclude from BS); owned dio ~3d = thread/lining/buttons only; near-zero receivables (paid on delivery, small advance common); payables minimal; fixed assets = sewing/overlock/interlock machines, iron & table, cabinets (primary collateral)
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 price_board 5 machinery 6 job_ticket 7 qr_code 8 utility_meter 9 rental_agreement 10 gst_board 11 udyam
Next-photo evidence · Eq 7
Order/job-ticket book tally Observed
Counts garments/day from pending job slips.
Stitching rate card / price board Observed
Fixes charge per garment by type (blouse/suit/alteration).
Delivery-date register Claim
Bounds working days incl. festive/wedding peaks.
Machine & workstation count Observed
Installed machines cap plausible daily throughput.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| UPI/QR settlement | garment count & charge → banked collections; cash residual | strong |
| Electricity bill | machine load → number of active machines/throughput sanity; owned/rented | medium |
| GST 3B / Udyam | declared turnover (often below-threshold/composition) & vintage | medium |
| Rental agreement | shop rent + deposit (BS) | medium |
Activity signals
| Footfall | 3 timed exterior/interior captures (weekday, weekend, festive week) → job-intake curve; cross-check pending-rack count vs UPI collections |
| B2B / counterparties | mostly B2C; if job-work for boutiques/exporters, count institutional counterparties from delivery challans/GSTR-1 |
Variance path: UPI collections + job-book + machine count → ≤15%; photo-only ≈ ±25% until job-ticket tally fixes garment count; strong festive/wedding seasonality — smooth with delivery register; watch owner-only labour where charge captures unpaid proprietor time
Earns a commission/margin on gross bookings (air, hotel, holiday packages); collects customer advances before travel and remits to suppliers, so turnover recognised is gross transaction value while the retained margin is the true income.
Turnover · metro₹5.10 Cr
Turnover · non-metro₹1.98 Cr
EBITDA · metro₹17.85 L
Half-width (photo)±189%→20%
Revenue drivers · metro clients(jobs)/day × avg fee × operating days
| Driver | Class | lo | base | hi | |
|---|
| Bookings / day | Observed | 6 | 10 | 18 | count |
| Avg gross ticket value | Claim | 8000 | 15000 | 32000 | ₹ |
| Operating days / yr | Claim | 300 | 340 | 360 | days |
Registry: gm [0.08, 0.11, 0.14] · opex [0.055, 0.075, 0.095] · DIO 0d · DSO 7d · DPO 15d · η 0.05
Occupancy — owned vs rented
| Rent/mo · metro | ₹30,000 / ₹60,000 / ₹1.20 L |
| Rent/mo · non-metro | ₹8,000 / ₹18,000 / ₹35,000 |
| Deposit → BS asset | 6 months |
Owned signal: electricity bill in proprietor's name + no rental agreement in Mitra pack → owned; substitute a fixed-asset/collateral note for rent
Balance-sheet build
no owned inventory (dio 0); receivables = corporate-account credit (dso ~7d) net against large customer-advance liability (negative operating WC in season); payables = supplier/consolidator credit 10–20d; fixed assets = office fit-out, computers, GDS terminal; float held for travel dates is a liability, not equity
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 price_board 5 qr_code 6 booking_register 7 gst_board 8 utility_meter 9 rental_agreement 10 pukka_invoice 11 licence 12 udyam
Next-photo evidence · Eq 7
GDS/PNR issuance summary (monthly) External
Airline/GDS PNR count bounds bookings/day.
Booking invoice sample (fare + commission) Observed
Fixes avg gross ticket value & retained margin.
12-month booking ledger (seasonality) Claim
Bounds effective operating days across peak/lean.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| GST 3B/GSTR-1 | declared commission/GTV turnover vs photo-derived bookings×ticket (concordance) | strong |
| Account-Aggregator bank feed | customer advances in / supplier remittances out → true retained margin & float | strong |
| UPI/QR settlement | card/UPI collections vs booking count; cash residual | medium |
| Rental agreement | office rent expense + deposit (BS) | medium |
| Udyam / IATA-agency licence | vintage, accreditation → supplier-credit access | medium |
Activity signals
| Footfall | walk-in bookings low; 2 timed interior captures (peak season week, lean week) cross-check counter activity vs PNR count |
| B2B / counterparties | corporate travel accounts material — count counterparties from GSTR-1 B2B invoices; large-value monthly settlements indicate corporate desk vs retail leisure mix |
Variance path: GST+AA advance/remittance reconciliation → ≤12% on retained margin; photo-only ≈ ±30% until PNR count + invoice sample close bookings and ticket; seasonal skew (summer/festival/wedding travel) requires 12-mo ledger to avoid over-reading a peak month
Small batches of students pay a monthly fee across the academic year; revenue = enrolled students × monthly fee × active months, tutor cost is the main variable expense.
Turnover · metro₹18.38 L
Turnover · non-metro₹4.50 L
EBITDA · metro₹4.23 L
Half-width (photo)±136%→22%
Revenue drivers · metro clients(jobs)/day × avg fee × operating days
| Driver | Class | lo | base | hi | |
|---|
| Enrolled students (batches × students) | Claim | 40 | 70 | 120 | students |
| Average monthly fee / student | Claim | 1500 | 2500 | 4200 | ₹ |
| Active academic months / yr | Benchmark | 9 | 10.5 | 11 | months |
Registry: gm [0.52, 0.6, 0.68] · opex [0.3, 0.37, 0.44] · DIO 0d · DSO 6d · DPO 5d · η 0.8
Occupancy — owned vs rented
| Rent/mo · metro | ₹12,000 / ₹25,000 / ₹55,000 |
| Rent/mo · non-metro | ₹3,000 / ₹7,000 / ₹15,000 |
| Deposit → BS asset | 4 months |
Owned signal: residential electricity tariff + no commercial rental agreement → home-run/owned; substitute a fixed-asset note (furniture only) for rent
Balance-sheet build
zero inventory; SEASONAL — revenue concentrated in ~10 academic months with a summer dip, so annualise on active months not 365d; small receivables from delayed monthly fees (DSO ~5–6d); negligible payables; fixed assets = benches, boards, fans
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 batch_timetable 5 enrolment_register 6 price_board 7 qr_code 8 utility_meter 9 udyam 10 pukka_invoice
Next-photo evidence · Eq 7
Enrolment / attendance register Observed
Names/heads across batches tighten enrolled-student count.
Batch timetable board (batches × slots) Observed
Number of batches × seat count caps enrolment.
Fee receipt book / fee slip Observed
Grade-wise slips constrain average monthly fee.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| UPI/QR settlement | monthly fee collections → enrolled-student count × fee | strong |
| AA bank feed | seasonal collection pattern (dip in summer break) → active months | medium |
| Electricity bill | residential vs commercial → premises type; small load | medium |
| Udyam | registered education/coaching activity; vintage | weak |
Activity signals
| Footfall | 2 timed exterior captures at batch changeover (after-school evening slots) → batch-size curve; cross-check vs enrolment register |
| B2B / counterparties | n/a (B2C parents) |
Variance path: enrolment register + fee receipts close half-width to ≤15%; photo-only wide (±30%) due to seasonality and unobserved batch count — timetable capture is the key next photo
Workshop bays process repair/service jobs — bays × jobs/day × average job value (spares + labour) — constrained by bay count, equipment and mechanic availability.
Turnover · metro₹80.40 L
Turnover · non-metro₹32.17 L
EBITDA · metro₹11.26 L
Half-width (photo)±137%→22%
Revenue drivers · metro clients(jobs)/day × avg fee × operating days
| Driver | Class | lo | base | hi | |
|---|
| Jobs / day (bays × throughput) | Observed | 8 | 12 | 20 | jobs |
| Average job value (spares + labour) | Claim | 1200 | 2000 | 3500 | ₹ |
| Operating days / yr | Claim | 300 | 335 | 355 | days |
Registry: gm [0.34, 0.42, 0.5] · opex [0.22, 0.28, 0.34] · DIO 35d · DSO 18d · DPO 30d · η 2
Occupancy — owned vs rented
| Rent/mo · metro | ₹40,000 / ₹80,000 / ₹1.60 L |
| Rent/mo · non-metro | ₹10,000 / ₹22,000 / ₹45,000 |
| Deposit → BS asset | 6 months |
Owned signal: commercial/industrial electricity bill in proprietor's name + no rental agreement → owned; substitute a fixed-asset/collateral note (equipment + land) for rent
Balance-sheet build
spares inventory material (DIO ~30–35d) via parts-rack worksheet; receivables from fleet/corporate & insurance jobs (DSO ~12–18d); spares-supplier payables 26–30d; fixed assets = lifts, compressors, diagnostic tools, welding — equipment as collateral
Guided capture — photo order
1 exterior 2 neighbourhood 3 workshop_bay 4 machinery 5 spares_rack 6 job_card_register 7 price_board 8 qr_code 9 utility_meter 10 gst_board 11 pukka_invoice 12 udyam
Next-photo evidence · Eq 7
Workshop bay & lift photo (capacity) Observed
Bays × lifts cap jobs/day.
Job-card register (7-day) Observed
Booked job cards tighten daily throughput.
Job invoice sample (spares vs labour split) Observed
Invoice mix constrains average job value and margin split.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| GST 3B/GSTR-1 | declared turnover + input credit on spares vs photo estimate (concordance) | strong |
| UPI/QR settlement | job payments → banked turnover; cash jobs = residual | strong |
| e-way / spares purchase invoices | spares inbound → parts throughput & COGS floor | medium |
| Electricity bill | compressor/lift load → bay-count sanity; owned/rented | medium |
Activity signals
| Footfall | 2 timed exterior captures (morning drop-off, evening pickup) → vehicle-in/out count; cross-check vs job-card register & UPI |
| B2B / counterparties | fleet/dealer AMC contracts drive receivables — estimate counterparties from GSTR-1 B2B invoices |
Variance path: bay count + job-card register + invoice sample close half-width to ≤15%; photo-only ±25–30% until spares/labour split and cash-job share are pinned
Operator owns/runs a small fleet of cars driven on aggregator apps, earning fare per trip; revenue = cars × trips/day × avg fare × utilisation, eaten into by fuel (dominant COGS), 20–25% aggregator commission, driver wages and heavy vehicle-loan EMI.
Turnover · metro₹37.98 L
Turnover · non-metro₹14.37 L
EBITDA · metro₹5.32 L
Half-width (photo)±194%→31%
Revenue drivers · metro vehicles × trips/veh/day × avg realisation × utilisation × operating days
| Driver | Class | lo | base | hi | |
|---|
| Cars in fleet | Observed | 2 | 4 | 8 | count |
| Trips / vehicle / day | Claim | 14 | 19 | 24 | count |
| Average fare / trip | Claim | 160 | 210 | 280 | ₹ |
| Utilisation (on-shift days) | Claim | 0.55 | 0.68 | 0.8 | × |
| Operating days / yr | Claim | 330 | 350 | 362 | days |
Registry: gm [0.55, 0.62, 0.68] · opex [0.42, 0.48, 0.53] · DIO 3d · DSO 7d · DPO 4d · η 0.05
Occupancy — owned vs rented
| Rent/mo · metro | ₹4,000 / ₹12,000 / ₹30,000 |
| Rent/mo · non-metro | ₹1,500 / ₹5,000 / ₹12,000 |
| Deposit → BS asset | 3 months |
Owned signal: electricity bill in proprietor's name for a residence/plot + no yard rental agreement → parked at owned premises; substitute a fixed-asset/collateral note for rent
Balance-sheet build
Cars are the principal fixed asset AND collateral; matched by vehicle-loan liabilities (EMI a major fixed obligation, tracked via AA feed, not inside opex). Near-zero inventory (only spares/tyres, dio ≈ 3). Receivables short (aggregator settles T+1 to weekly, dso ≈ 6–7). Fuel payables small (dpo ≈ 4). Parking-yard deposit a minor BS asset.
Guided capture — photo order
1 exterior 2 vehicle_fleet 3 odometer_permit 4 fuel_log 5 qr_code 6 licence 7 gst_board 8 udyam
Next-photo evidence · Eq 7
Aggregator earnings dashboard (weekly trips) External
Constrains trips/vehicle/day directly from platform data.
Fuel bills / FASTag km log External
Km run vs idle days → utilisation.
RC + taxi-permit count for fleet External
Fixes fleet size from registry.
Trip-receipt / fare sample Observed
Constrains average fare per trip.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| Aggregator settlement statement | trips & fare → banked fare income; commission % visible | strong |
| AA bank feed (EMI outflows) | vehicle-loan EMI count → number of financed cars; net cash after EMI | strong |
| RC / permit registry | vehicles (fleet count) — collateral identification | strong |
| FASTag / fuel bills | util → km run vs idle; energy proxy (fuel, not grid) | medium |
| GST 3B/GSTR-1 | declared turnover vs fare-derived (concordance) | medium |
Activity signals
| Footfall | n/a — asset-utilisation business, not footfall; fleet count established via RC/permits, utilisation via trip logs + FASTag/fuel km |
| B2B / counterparties | largely B2C via aggregator; any corporate-tie-up trips visible in GSTR-1 counterparties |
Variance path: Aggregator dashboard + AA EMI feed + RC count → ≤10–12%; photo-only ≈ ±30% until trip dashboard and fuel/km log close the trips and utilisation gaps.
Operator runs commercial vehicles on hire or fixed contract (e.g. staff/school transport, project logistics, bulk carriage); revenue = vehicles × trips/day × realisation × utilisation, with fuel the dominant COGS plus toll, maintenance, driver wages and vehicle-loan EMI. Note: if the borrower is instead a commercial-vehicle DEALERSHIP, this is a retail archetype (unit sales × margin) — the fleet-operator model here does not apply.
Turnover · metro₹1.54 Cr
Turnover · non-metro₹64.35 L
EBITDA · metro₹23.09 L
Half-width (photo)±437%→39%
Revenue drivers · metro vehicles × trips/veh/day × avg realisation × utilisation × operating days
| Driver | Class | lo | base | hi | |
|---|
| Commercial vehicles in fleet | Observed | 2 | 6 | 15 | count |
| Trips / vehicle / day | Claim | 1 | 1.8 | 3 | count |
| Avg realisation / trip | Claim | 3500 | 6000 | 10000 | ₹ |
| Utilisation (deployed vs idle) | Claim | 0.55 | 0.72 | 0.85 | × |
| Operating days / yr | Claim | 300 | 330 | 355 | days |
Registry: gm [0.44, 0.5, 0.56] · opex [0.3, 0.35, 0.4] · DIO 6d · DSO 45d · DPO 12d · η 0.04
Occupancy — owned vs rented
| Rent/mo · metro | ₹8,000 / ₹20,000 / ₹55,000 |
| Rent/mo · non-metro | ₹3,000 / ₹9,000 / ₹20,000 |
| Deposit → BS asset | 3 months |
Owned signal: no yard rental agreement + plot property tax / electricity bill in proprietor's name → owned depot; substitute a fixed-asset/collateral note for rent
Balance-sheet build
Commercial vehicles are the principal fixed asset AND collateral, matched by vehicle-loan liabilities (EMI a major fixed obligation via AA feed, outside opex). Low inventory (spares/tyres, dio ≈ 5–6). Contract/hire receivables billed monthly drive WCR (dso ≈ 38–45). Fuel/toll payables modest (dpo ≈ 10–12). Depot deposit a minor BS asset if rented. (If a dealership instead: inventory of vehicles, floor-plan financing and DSO change entirely.)
Guided capture — photo order
1 exterior 2 vehicle_fleet 3 odometer_permit 4 fuel_log 5 licence 6 gst_board 7 udyam
Next-photo evidence · Eq 7
Trip sheet / contract log Observed
Constrains trips/vehicle/day.
FASTag crossings / odometer reading External
Km run vs idle → utilisation.
RC + permit count for fleet External
Fixes fleet size from registry.
Hire / contract & rate sample Observed
Constrains realisation per trip.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| FASTag / toll data | util & trips → deployment frequency and routes | strong |
| RC / permit registry | vehicles (fleet count) — collateral identification | strong |
| AA bank feed (EMI outflows) | vehicle-loan EMI count → number of financed vehicles | strong |
| Hire / contract agreements + GSTR-1 | fare realisation; contract counterparty count | strong |
| Fuel bills | fuel = dominant COGS; km run; energy proxy (diesel, not grid) | medium |
Activity signals
| Footfall | n/a — asset-utilisation; fleet count via RC/permits, utilisation via FASTag + odometer/fuel logs |
| B2B / counterparties | count hire/contract counterparties from agreements + GSTR-1 to bound deployment and receivable concentration |
Variance path: FASTag + RC count + contract/GSTR-1 → ≤12–15%; photo-only ≈ ±30–35% until trip sheet and FASTag/odometer close the trips and utilisation gaps (idle vehicles are the key uncertainty).
A hub/franchise runs a fleet of bikes/vans with riders delivering parcels; revenue = riders × parcels/day × rate per parcel × utilisation — high utilisation but thin per-parcel margin, with rider wages, franchise/hub fee, fuel and maintenance the main costs.
Turnover · metro₹57.06 L
Turnover · non-metro₹23.12 L
EBITDA · metro₹4.56 L
Half-width (photo)±334%→32%
Revenue drivers · metro vehicles × trips/veh/day × avg realisation × utilisation × operating days
| Driver | Class | lo | base | hi | |
|---|
| Riders / delivery vehicles | Observed | 4 | 10 | 25 | count |
| Parcels / rider / day | Claim | 40 | 70 | 110 | count |
| Rate per parcel | Claim | 18 | 28 | 42 | ₹ |
| Utilisation (working riders/day) | Claim | 0.7 | 0.82 | 0.92 | × |
| Operating days / yr | Claim | 340 | 355 | 365 | days |
Registry: gm [0.4, 0.46, 0.52] · opex [0.33, 0.38, 0.43] · DIO 2d · DSO 22d · DPO 8d · η 0.08
Occupancy — owned vs rented
| Rent/mo · metro | ₹10,000 / ₹25,000 / ₹60,000 |
| Rent/mo · non-metro | ₹4,000 / ₹10,000 / ₹22,000 |
| Deposit → BS asset | 4 months |
Owned signal: hub premises usually rented — rental agreement + DISCOM bill in landlord/company name confirm rent; owned hub is rare (then use fixed-asset note)
Balance-sheet build
Bikes/vans are fixed assets and collateral where financed (van EMI via AA feed, outside opex); many bikes are rider-owned so fleet asset base can be light. Negligible inventory (dio ≈ 2). Receivables = COD float + corporate/franchise billing (dso ≈ 18–22). Payables short (dpo ≈ 7–8). Hub deposit a BS asset.
Guided capture — photo order
1 exterior 2 vehicle_fleet 3 odometer_permit 4 fuel_log 5 qr_code 6 licence 7 gst_board 8 udyam
Next-photo evidence · Eq 7
Hub manifest / parcels-scanned report External
Constrains parcels/rider/day from scan data.
Rider roster / attendance sheet Observed
Active riders = delivery capacity.
Client rate card / franchise slab External
Constrains per-parcel rate.
Fuel bills / route-km log External
Route km vs idle → utilisation.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| Hub / franchise scan dashboard | trips (parcels) → daily volume; SLA and success rate | strong |
| Client / franchise rate card | fare → per-parcel realisation | strong |
| AA bank feed / franchise settlement | COD remittance, franchise fee, EMI on vans | strong |
| RC count (vans/bikes) | vehicles → fleet size / collateral | medium |
| Fuel bills | util & energy proxy (petrol, not grid); route km | medium |
| GST 3B/GSTR-1 | declared turnover vs volume-derived (concordance) | medium |
Activity signals
| Footfall | n/a — throughput business; capacity from rider roster, volume from hub scan manifests, utilisation from fuel/route km |
| B2B / counterparties | e-commerce / corporate client count from GSTR-1 counterparties bounds parcel volume and revenue concentration |
Variance path: Hub scan manifest + rate card + rider roster → ≤10–12%; photo-only ≈ ±30% until parcels/rider/day and active-rider count are pinned (thin margin makes rate accuracy matter most).
Operator owns trucks/tempos moving freight for businesses; revenue = vehicles × loads/day × avg freight realisation × utilisation, with fuel the dominant COGS and toll, maintenance, driver wages and vehicle-loan EMI as the main costs; long B2B freight receivables strain working capital.
Turnover · metro₹1.40 Cr
Turnover · non-metro₹51.33 L
EBITDA · metro₹27.95 L
Half-width (photo)±389%→39%
Revenue drivers · metro vehicles × trips/veh/day × avg realisation × utilisation × operating days
| Driver | Class | lo | base | hi | |
|---|
| Trucks / tempos in fleet | Observed | 2 | 5 | 12 | count |
| Loads / vehicle / day | Claim | 1.2 | 2.2 | 3.5 | count |
| Avg freight realisation / load | Claim | 3000 | 5500 | 9000 | ₹ |
| Utilisation (loaded, not empty-return) | Claim | 0.55 | 0.7 | 0.82 | × |
| Operating days / yr | Claim | 300 | 330 | 355 | days |
Registry: gm [0.48, 0.55, 0.6] · opex [0.3, 0.35, 0.4] · DIO 6d · DSO 55d · DPO 12d · η 0.04
Occupancy — owned vs rented
| Rent/mo · metro | ₹8,000 / ₹20,000 / ₹50,000 |
| Rent/mo · non-metro | ₹3,000 / ₹8,000 / ₹18,000 |
| Deposit → BS asset | 3 months |
Owned signal: no yard rental agreement in pack + property tax / electricity bill for the plot in proprietor's name → owned yard; substitute a fixed-asset/collateral note for rent
Balance-sheet build
Trucks/tempos are the principal fixed asset AND collateral, matched by vehicle-loan liabilities (EMI a major fixed obligation via AA feed, outside opex). Low inventory (spares/tyres, dio ≈ 5–6). Long B2B freight receivables dominate WCR (dso ≈ 45–55). Fuel/toll payables modest (dpo ≈ 10–12). Yard deposit a minor BS asset if rented.
Guided capture — photo order
1 exterior 2 vehicle_fleet 3 odometer_permit 4 fuel_log 5 licence 6 gst_board 7 udyam
Next-photo evidence · Eq 7
Trip register / lorry receipts (LR) Observed
Constrains loads/vehicle/day.
FASTag toll-crossings log External
Trip frequency + empty-return → utilisation.
RC + national-permit count External
Fixes fleet size from registry.
Freight invoice / rate sample Observed
Constrains freight realisation per load.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| FASTag / toll data | util & trips → trip frequency, routes, empty-return share | strong |
| RC / national-permit registry | vehicles (fleet count) — collateral identification | strong |
| AA bank feed (EMI outflows) | vehicle-loan EMI count → number of financed trucks | strong |
| GST e-way bills / GSTR-1 | fare & throughput; number of B2B freight counterparties | strong |
| Fuel bills | fuel = dominant COGS; km run; energy proxy (diesel, not grid) | medium |
Activity signals
| Footfall | n/a — asset-utilisation; fleet count via RC/national permits, utilisation via FASTag crossings + fuel/km logs |
| B2B / counterparties | count B2B freight counterparties from e-way bills + GSTR-1 to bound throughput and receivable concentration |
Variance path: FASTag + e-way/GSTR-1 + RC count → ≤12–15%; photo-only ≈ ±30–35% until trip register and FASTag log close the loads/day and utilisation gaps (empty-return is the biggest uncertainty).
Rents refrigerated capacity (per MT or sq ft) to farmers/traders at a seasonal rate scaled by occupancy; revenue is capacity × rate × occupancy, but a very large power bill and seasonality make the electricity signal decisive.
Turnover · metro₹91.00 L
Turnover · non-metro₹55.89 L
EBITDA · metro₹20.93 L
Half-width (photo)±146%→18%
Revenue drivers · metro sales per sqft/day × usable area × operating days
| Driver | Class | lo | base | hi | |
|---|
| Effective rent yield / sq ft / day (net of vacancy) | Claim | 0.8 | 1.3 | 2 | ₹ |
| Rentable cold capacity (chamber floor) | Observed | 10000 | 20000 | 40000 | sq ft |
| Operating days / yr | Claim | 320 | 350 | 365 | days |
Registry: gm [0.45, 0.58, 0.68] · opex [0.25, 0.35, 0.45] · DIO 0d · DSO 32d · DPO 15d · η 3.2
Occupancy — owned vs rented
| Rent/mo · metro | ₹24 / ₹40 / ₹60 |
| Rent/mo · non-metro | ₹15 / ₹27 / ₹42 |
| Deposit → BS asset | 2 months |
Owned signal: high sanctioned load + property-tax receipt in owner's name and no lease-as-tenant → building + plant are owned; treat as fixed asset / collateral (major security), rent received is revenue
Balance-sheet build
no own inventory (DIO 0) — goods are depositors' (held on behalf, off-book); receivables = seasonal storage dues (DSO ~32d); the building + refrigeration plant are the dominant fixed assets and usual COLLATERAL (value plant separately, note refrigerant type/age); depositor advances are a liability
Guided capture — photo order
1 exterior 2 neighbourhood 3 cold_room 4 occupancy_gauge 5 utility_meter 6 machinery 7 rental_agreement 8 interior 9 gst_board 10 pukka_invoice 11 udyam 12 licence
Next-photo evidence · Eq 7
DISCOM bill (12-month kWh profile) External
Compressor load tracks occupied capacity — strongest signal; seasonality visible.
Chamber occupancy read (bags/pallets vs capacity) Observed
Occupancy % is the dominant yield driver.
Storage ledger / rental register Claim
Rate/MT × stored quantity → revenue directly.
Chamber capacity measurement (MT / sq ft) Observed
Fixes rentable cold capacity.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| Electricity/DISCOM bill | kWh vs occupied capacity (eta ~3+) — decisive triangulation; reveals seasonality | strong |
| Storage rental ledger | rate/MT × stored qty × tenure → revenue | strong |
| GST 3B/GSTR-1 | rental/service turnover vs derived (concordance) | strong |
| AA bank feed | seasonal rent inflows; advance bookings vs capacity | medium |
Activity signals
| Footfall | n/a; inbound/outbound truck timing marks the fill (post-harvest) and draw-down cycle |
| B2B / counterparties | count depositors from storage ledger + GSTR-1; occupancy % = stored MT ÷ capacity is the key utilisation measure — peak-season fill vs annual average matters for a seasonal facility |
Variance path: power profile + storage ledger + GST → ≤10%; strongly seasonal so quote peak-fill AND annualised occupancy; photo-only ≈ ±30% until the DISCOM kWh profile and occupancy gauge close the yield gap
Buys fast-moving branded stock on short company credit and pushes it to retailers via van/beat sales on a very thin distributor margin plus scheme income; profit is made on high inventory turn and disciplined retailer collections.
Turnover · metro₹4.90 Cr
Turnover · non-metro₹2.90 Cr
EBITDA · metro₹7.35 L
Half-width (photo)±105%→15%
Revenue drivers · metro sales per sqft/day × usable area × operating days
| Driver | Class | lo | base | hi | |
|---|
| Dispatch value / sq ft / day | Claim | 25 | 40 | 60 | ₹ |
| Godown usable area | Observed | 2000 | 3500 | 5500 | sq ft |
| Operating days / yr | Claim | 330 | 350 | 362 | days |
Registry: gm [0.04, 0.055, 0.07] · opex [0.025, 0.04, 0.05] · DIO 15d · DSO 12d · DPO 14d · η 0.32
Occupancy — owned vs rented
| Rent/mo · metro | ₹40,000 / ₹75,000 / ₹1.50 L |
| Rent/mo · non-metro | ₹12,000 / ₹28,000 / ₹55,000 |
| Deposit → BS asset | 6 months |
Owned signal: electricity in proprietor's name + no lease → owned godown; else lease deposit is a BS asset
Balance-sheet build
inventory fast (DIO ~15d) = measured stock worksheet; receivables = retailer credit (DSO ~12d, mostly cash/UPI at drop); payables = company credit (DPO ~14d) → tight trade cycle, modest WC; fixed assets = delivery vans/tempo (financeable), racking, DMS handhelds
Guided capture — photo order
1 exterior 2 neighbourhood 3 storage 4 dispatch 5 vehicle_fleet 6 display 7 qr_code 8 gst_board 9 utility_meter 10 pukka_invoice 11 kacha_bill 12 udyam
Next-photo evidence · Eq 7
Daily beat / van load-out sheet Observed
Per-van dispatch value → throughput per sqft.
Company primary-purchase invoices External
Inward purchases + turn confirm dispatch volume.
Active retailer count (GSTR-1/DMS) External
Outlets served bound daily secondary sales.
Godown area measurement Observed
Fixes usable stacked area.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| Company DMS / primary invoices | primary purchases + scheme income → revenue base | strong |
| e-way bills | inward primary + outward secondary consignments → throughput | strong |
| GST 3B/GSTR-1 | declared turnover + active retailer count vs derived | strong |
| AA bank feed | retailer collections vs sales; distributor-credit discipline | medium |
Activity signals
| Footfall | n/a (B2B); van load-out and return timing at the bay is a throughput check |
| B2B / counterparties | active outlets from GSTR-1 + DMS + beat register × drop size → bounds daily secondary sales; number of vans caps reach |
Variance path: DMS + e-way + GST + bank concordance → ≤10%; photo-only ≈ ±25% until beat/van sheet and primary invoices close the psf gap
Owns warehouse space and earns rent per sq ft per month scaled by occupancy; near-zero COGS and the building itself is the asset, so revenue is effectively rentable area × rent × occupancy.
Turnover · metro₹27.30 L
Turnover · non-metro₹9.58 L
EBITDA · metro₹15.02 L
Half-width (photo)±135%→13%
Revenue drivers · metro sales per sqft/day × usable area × operating days
| Driver | Class | lo | base | hi | |
|---|
| Effective rent yield / sq ft / day (net of vacancy) | Claim | 0.3 | 0.5 | 0.75 | ₹ |
| Rentable area | Observed | 8000 | 15000 | 30000 | sq ft |
| Operating days / yr | Claim | 360 | 364 | 365 | days |
Registry: gm [0.75, 0.85, 0.92] · opex [0.2, 0.3, 0.42] · DIO 0d · DSO 22d · DPO 10d · η 0.15
Occupancy — owned vs rented
| Rent/mo · metro | ₹15 / ₹22 / ₹32 |
| Rent/mo · non-metro | ₹6 / ₹10 / ₹16 |
| Deposit → BS asset | 3 months |
Owned signal: electricity + property-tax receipt in owner's name and NO lease-as-tenant → building is owned; treat as fixed asset / collateral (often the main security), and rent received is revenue not opex
Balance-sheet build
no inventory (DIO 0); receivables = rent arrears (DSO ~22d); minimal payables; the building is the dominant fixed asset and usual COLLATERAL — value it and note tenure/title; security deposits held are a liability
Guided capture — photo order
1 exterior 2 neighbourhood 3 storage 4 occupancy_gauge 5 rental_agreement 6 interior 7 utility_meter 8 gst_board 9 pukka_invoice 10 udyam 11 licence
Next-photo evidence · Eq 7
Occupancy read (bays/racks filled vs total) Observed
Occupancy % is the dominant yield driver — filled vs empty bays.
Tenant rent ledger / lease schedule Claim
Contracted rent/sqft × occupied area → revenue directly.
Rentable-area measurement Observed
Fixes lettable sq ft vs gross built-up.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| Tenant rental agreements | contracted rent/sqft, tenure, escalation → revenue | strong |
| AA bank feed | monthly rent inflows vs contracted rent; arrears/vacancy | strong |
| GST 3B/GSTR-1 | rental turnover (18% GST on commercial rent) vs derived | strong |
| Property-tax receipt / electricity | ownership → collateral; connected-load floor-size sanity | medium |
Activity signals
| Footfall | n/a; vehicle in/out at gate is a soft occupancy signal only |
| B2B / counterparties | number of tenants from lease schedule + GSTR-1; occupancy % = occupied ÷ rentable area is the key throughput/utilisation measure and single-tenant reliance is a risk flag |
Variance path: lease schedule + bank rent inflows + GST → ≤8% (rental income is contractual); photo-only ≈ ±20% until occupancy gauge and area measure pin the yield
Buys goods in bulk on supplier credit and resells to B2B buyers on a thin buy-sell margin; profit rides on inventory turn and the receivables-vs-payables spread, not price.
Turnover · metro₹1.45 Cr
Turnover · non-metro₹67.20 L
EBITDA · metro₹3.63 L
Half-width (photo)±113%→20%
Revenue drivers · metro sales per sqft/day × usable area × operating days
| Driver | Class | lo | base | hi | |
|---|
| Sales / sq ft / day | Claim | 12 | 20 | 30 | ₹ |
| Usable trading/storage area | Observed | 1200 | 2200 | 3500 | sq ft |
| Operating days / yr | Claim | 300 | 330 | 355 | days |
Registry: gm [0.05, 0.07, 0.1] · opex [0.03, 0.045, 0.06] · DIO 40d · DSO 38d · DPO 34d · η 0.3
Occupancy — owned vs rented
| Rent/mo · metro | ₹30,000 / ₹55,000 / ₹1.10 L |
| Rent/mo · non-metro | ₹8,000 / ₹16,000 / ₹30,000 |
| Deposit → BS asset | 6 months |
Owned signal: electricity bill in proprietor's name + no rental agreement in Mitra pack → owned godown; substitute a fixed-asset/collateral note for rent
Balance-sheet build
inventory = measured stock worksheet (Observed) overrides benchmark DIO (~40d); receivables = B2B buyer credit (DSO ~35d); payables = supplier credit (DPO ~32d) → positive trade cycle needs WC funding; fixed assets = racking, weighing, small handling gear
Guided capture — photo order
1 exterior 2 neighbourhood 3 interior 4 storage 5 dispatch 6 price_board 7 qr_code 8 gst_board 9 utility_meter 10 pukka_invoice 11 kacha_bill 12 udyam
Next-photo evidence · Eq 7
Dispatch register / outward invoices Observed
Bounds daily throughput → the dominant psf spread.
Usable-area pace-out / plan Observed
Fixes usable area vs gross floor.
GSTR-1 B2B counterparty count External
Number of active buyers cross-checks throughput.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| GST 3B/GSTR-1 | declared turnover + B2B counterparty count vs photo-derived throughput (concordance) | strong |
| e-way bills | outward consignment volume → throughput (psf) sanity | strong |
| AA bank feed | banked collections vs GST turnover; working-capital swings | medium |
| Electricity bill | connected load → floor-size sanity; owned/rented | medium |
Activity signals
| Footfall | n/a (B2B); loading-bay activity photo at peak dispatch hour is a soft check only |
| B2B / counterparties | count distinct buyers from GSTR-1 + dispatch register + e-way bills → bounds daily throughput and concentration risk |
Variance path: GST + e-way + bank concordance → ≤12%; photo-only ≈ ±25–30% until dispatch register + area measure close the psf/area gap
Holds a principal's stock in a large godown and earns a fixed commission on throughput plus a small trading spread; large rupee flow but the agent's own margin is thin and receivables/payables settle against the principal.
Turnover · metro₹4.69 Cr
Turnover · non-metro₹2.31 Cr
EBITDA · metro₹9.38 L
Half-width (photo)±110%→19%
Revenue drivers · metro sales per sqft/day × usable area × operating days
| Driver | Class | lo | base | hi | |
|---|
| Throughput value / sq ft / day | Claim | 18 | 28 | 42 | ₹ |
| Godown usable area | Observed | 3000 | 5000 | 8000 | sq ft |
| Operating days / yr | Claim | 310 | 335 | 358 | days |
Registry: gm [0.04, 0.06, 0.09] · opex [0.025, 0.04, 0.055] · DIO 28d · DSO 35d · DPO 22d · η 0.28
Occupancy — owned vs rented
| Rent/mo · metro | ₹60,000 / ₹1.20 L / ₹2.50 L |
| Rent/mo · non-metro | ₹18,000 / ₹40,000 / ₹80,000 |
| Deposit → BS asset | 6 months |
Owned signal: electricity + property-tax receipt in agent's name + no lease in Mitra pack → owned godown as collateral; else lease deposit is a BS asset
Balance-sheet build
principal stock is held-on-behalf (off-book or clearly segregated — do NOT count as own inventory); own inventory small (DIO ~28d); receivables = commission + trade dues (DSO ~35d); payables to principal (DPO ~22d); fixed assets = racking, handling equipment, godown if owned (collateral)
Guided capture — photo order
1 exterior 2 neighbourhood 3 storage 4 dispatch 5 interior 6 price_board 7 qr_code 8 gst_board 9 utility_meter 10 pukka_invoice 11 udyam 12 licence
Next-photo evidence · Eq 7
Dispatch / GRN register Observed
Daily inward/outward value → throughput per sqft.
Principal commission statement Claim
Commission-on-throughput ties revenue to volume directly.
Godown area measurement Observed
Fixes usable racked area.
e-way bill throughput External
Independent consignment-volume check.
Mitra data pack — cross-checks
| Source | Validates | Strength |
|---|
| Principal commission statement | commission income + throughput; strongest tie to revenue | strong |
| e-way bills | inward + outward consignment volume → psf throughput | strong |
| GST 3B/GSTR-1 | commission + trading turnover vs derived (concordance) | strong |
| Rental agreement | godown rent + deposit (BS); or owned-asset note | medium |
Activity signals
| Footfall | n/a (B2B); loading activity at dispatch bay is a soft throughput check |
| B2B / counterparties | distinct downstream stockists/retailers from dispatch register + e-way + GSTR-1 → bounds throughput; single-principal dependence is a concentration flag |
Variance path: commission statement + e-way + GST concordance → ≤10%; photo-only ≈ ±25% until dispatch register and principal statement close the psf gap