Edgify Financial Model
AI/ML Startup Financials (Free Excel Download)
Edge AI computer vision software that identifies non-barcoded produce at retail point-of-sale, trained locally on the device without cloud dependency.
professionals from Deloitte
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About this model
Edgify delivers edge AI software that identifies non-barcoded produce on existing retail point-of-sale hardware. The model trains locally from customer transactions, then shares weights across a store's devices without uploading store data to the cloud.
It supports cashier lanes, self-checkout, and PC scales, with a one-day-to-two-week integration followed by a four-to-six-week training phase. Edgify claims 99.98% recognition accuracy, 0.006-second recognition speed, and checkout-time reductions of up to 75%.
The ARR model is built around chain contracts, stores, and PoS devices, with a revenue ramp while each site learns. It tests sales-cycle length, devices per store, implementation capacity, device licensing, churn, expansion, software margins, engineering and sales staffing, cash burn, and runway.
A turnkey financial model
Live formulas, no hardcoded values
Outputs are driven by live formulas, so the workbook updates from its assumptions instead of relying on hardcoded results.
All assumptions in one tab
Inputs are clearly marked in the Assumptions tab and separated from calculations, making it clear what to change and what to leave intact.
Statements always balancing
For integrated-statement models, the balance sheet, cash flow, and supporting schedules tie through properly.
Distinct schedules for clarity
Debt, working capital, taxes, and cash flow can get messy quickly. We group calculations in clear schedules, not across disconnected tabs.
No hidden macros or external links
There are no unexplained external workbook links or macros to undermine auditability or portability.
Changes flow through the model
Update a key driver and see the impact carry through the forecast, financing, and return outputs. We never use hardcoded numbers in formulas.
About Edgify
edgify.ai
How to build a detailed financial model for Edgify
A complete walkthrough of the business, drivers, and assumptions behind the downloadable Edgify model - distilled from its pitch deck and publicly available information.
Product & value proposition
Edge AI software that runs on existing retail PoS hardware (lane cashier, self-checkout, PC scale) and uses computer vision to identify fresh produce (fruit & veg, bakery, fresh produce) without barcodes. Key differentiator: the model trains itself locally on each device using real customer transactions, then shares model weights (not data) across all PoS units in a store and across stores - no cloud upload required. Accuracy claimed: 99.98% / "100% over time".
Three deployment use cases:
- Lane PoS (cashier)
- Self-Checkout (SCO)
- PC Scale (F&V / Dairy)
Implementation timeline:
- Software integration: 1 day – 2 weeks (remote, no new infrastructure)
- Dormant/training phase: 4–6 weeks
- Active phase launch: 1 week
- Results review: 1 hour
Two proprietary IP components:
- Edgify Edge Training Loop - software loop that trains on any edge device with minimal processing overhead
- Edgify Collaborative Controller - aggregates models from all edge devices, combines into optimised model, redistributes
Traction & metrics
Quantitative performance claims (product, not financial):
- Accuracy (market standard baseline): 65%
- Accuracy (Edgify): 99.98%
- Checkout time reduction: up to 75%
- Recognition speed: 0.006 seconds per item
- Time to full produce recognition in store: ~30 days of shopping
Competition / moat
Competitive framing vs. cloud-based computer vision:
- Incumbents require millions of images sent to cloud for training; costly AI infrastructure and ongoing retraining.
- Edgify: trains on-device, no data leaves the store, continuous retraining on actual store conditions (lighting, angles, produce freshness stages), no cloud costs.
- Moat claims: on-device federated/distributed learning eliminates cloud dependency; proprietary collaborative controller IP; privacy-by-design (no data transfer); model improves automatically per store conditions.
No named competitors called out.
Recommended financial model
- Archetype + why: B2B SaaS ARR model with a per-device / per-store subscription unit. The product is software deployed on existing hardware - recurring licensing is the natural monetisation. The key value metric is number of PoS devices (or stores) under contract, making a seat/device-based SaaS ARR model the right archetype.
- Forecast horizon & granularity: 3 years monthly (Year 1–2) then annual (Year 3); monthly granularity needed to model ramp from dormant → active phase (~6–8 weeks per deployment) and cohort-based ARR recognition.
- Key drivers & assumptions:
| Driver | Value |
|---|---|
| Average PoS devices per store | 5–10 |
| Annual license fee per device | $X (unknown) |
| Average store count at contract signing (chain deal) | 10–50 stores |
| Sales cycle length | 3–6 months |
| Dormant + active phase ramp (weeks to revenue) | 6–9 weeks per site |
| Gross margin (software only, no hardware COGS) | 70–80% |
| Logo churn (annual) | 5–10% |
| Net revenue retention | 105–115% |
| Implementation/onboarding fee (one-time) | small relative to ARR |
| Checkout time reduction driving ROI for customer | 75% |
- Scenarios (Base / Bull / Bear - which variables flex):
- Base: Moderate enterprise deal velocity; 1–2 chain deals per quarter in Year 1; device count mid-range.
- Bull: Faster chain penetration (3–4 deals/quarter); higher device count per store; upsell to PC scale units.
- Bear: Long sales cycles (9–12 months); pilot-heavy before conversion; low initial device count commitments.
- Primary flex variables: new logos signed per quarter, avg devices per contract, ACV per device, ramp time to active phase.
- Required sheets / outputs:
- Assumptions dashboard (all drivers in one place)
- ARR build (new ARR, expansion ARR, churned ARR, net new ARR, closing ARR - by cohort/quarter)
- Revenue schedule (MRR → recognised revenue, accounting for dormant ramp period)
- P&L (Revenue, gross profit, S&M, R&D, G&A, EBITDA)
- Headcount plan (sales, implementation, engineering)
- Cash / runway (if raise size known - currently not in deck)
- Scenario toggle (Base / Bull / Bear)
Frequently asked
Is the Edgify financial model free?+
Yes. The Edgify model is a free Excel (.xlsx) download with live formulas. Sign up with your email and the workbook is yours to keep, review, and edit.
What's included in the model?+
A 5-year monthly forecast with P&L, cash flow and runway, valuation (exit multiple plus a DCF cross-check), MOIC/IRR returns, and unit economics, with live formulas throughout.
How was this model built?+
It was built from Edgify's pitch deck and publicly available information, then structured to investment-banking standards as a fully editable Excel model.
Can I change the assumptions?+
Yes. You can change assumptions and the live formulas will recalculate in the downloadable Excel model.
Have more financial modelling questions? Contact us
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