Stor.ai Financial Model
AI/ML Startup Financials (Free Excel Download)
B2B SaaS platform powering end-to-end digital commerce (engagement, merchandising, fulfillment) for grocery and retail chains.
professionals from Deloitte
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About this model
StorAI provides a customer-first digital-commerce platform for grocery retailers, spanning engagement, merchandising, frictionless shopping, and fulfilment. Its products include digital storefronts, Scan & Go, Smart Carts, a picking application, and integrations with POS and final-mile delivery partners.
The company charges a recurring per-store license with no transaction fees. It reports more than 300 retailers on the platform and over two million annual orders; a Yenot Bitan case study cites 25,000 monthly online orders, $185 average order value, and 50% picking-labour reduction.
The model follows retail logos, stores deployed, license fees, implementation ramp, expansion, and churn. It incorporates fulfilment-support and cloud costs, sales capacity, product and operational hiring, gross margin, cash burn, and runway, while treating order volume as an adoption and ROI driver rather than fee revenue.
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 Stor.ai
stor.ai
How to build a detailed financial model for Stor.ai
A complete walkthrough of the business, drivers, and assumptions behind the downloadable Stor.ai model - distilled from its pitch deck and publicly available information.
Product & value proposition
Four-pillar "Customer-First Commerce" platform for grocery retailers:
- Customer Engagement - unified product browsing, discovery, and shopping UX across all digital touchpoints.
- Digital Merchandising - brings trade-promo and shopper-marketing into the digital ecosystem; controls above-the-fold "prime real estate" in category/search results pages (84% engagement differential above vs. below fold, citing Nielsen Norman Group).
- Frictionless Shopping - mobile app, Scan & Go, Smart Carts.
- Fulfillment - three picking-mode app (Android/iOS) with POS integration, final-mile delivery partner integrations, MFC and dark-store capable. Delivers ~50% labor reduction for picking.
Revenue model
- Per-store license fee - recurring SaaS charge billed per retail location.
- No transaction fees - explicitly stated, differentiating from GMV-take-rate models.
- ROI to customer typically <12 months - used as a sales / conversion argument.
- Specific fee amounts ($ per store per month/year) not disclosed in deck.
Traction & metrics
- +300 retailers on platform (locations, not logo count)
- >2 million orders processed annually
- Company founded 2014
- Customer case study - Yenot Bitan (Israel's 2nd largest grocer, ~200 stores, ~$1.5B USD annual revenue, customer since 2018):
- ~25,000 orders/month via online channel
- AOV: $185, up 5% YoY
- Labor cost reduction: 50% via fulfillment app
- Geographies active: US, Canada, Europe, GCC, Israel
- Technology partners/integrations: Toshiba, Unilever, Trax, Microsoft, Salesforce
Unit economics
- AOV: $185 (from Yenot Bitan case study, ~5% YoY growth)
- Labor reduction: 50% for order picking post-implementation
- Customer ROI: <12 months
Competition / moat
- Deep integration with POS systems and MFC/dark-store infrastructure
- Incumbent customer relationships (Yenot Bitan since 2018)
- Dedicated dev team per customer (stated as feature)
- GDPR and ISO 27001 compliance certifications
Team & funding ask / use of funds
Team (key executives):
- Orlee Tal - CEO; ex-Retalix, Google lecturer, 20+ years retail
- Morris Azulay - CFO; funding/M&A background, retail
- Irit Fridlis - VP Strategy; ex-Shufersal, 15+ years retail
Recommended financial model
- Archetype + why: B2B SaaS per-unit (per-store) ARR model - revenue is a recurring license fee per retail location; no transaction revenue. Model should track store-count as the primary growth unit (new logos × avg stores/logo), layering on expansion (additional modules/stores per existing customer) and churn.
- Forecast horizon & granularity: 3 years monthly (Year 1–2 monthly, Year 3 annual summary is acceptable). Monthly resolution matters for cash-flow visibility on a license-fee business where sales cycles to large grocery chains can be 6–12 months.
- Key drivers & assumptions:
- Number of retailer logos (new logos/year): start at ~5–8 new logos/year based on +300 locations across an implied base of mid-size to large chains; refine when logo count is disclosed
- Average stores per logo: ~10–30 stores/logo, reflecting mid-market grocery chains; large anchor like Yenot Bitan (~200 stores) skews high
- License fee per store per month: ~$500–$1,500/store/month, consistent with grocery retail SaaS comps (Instacart Storefront, Mercaux, etc.); no deck figure
- Annual churn rate (logo-level): ~5–10% given enterprise grocery contracts and deep POS integrations; refine from actuals
- Gross margin: ~65–75% for SaaS with dedicated dev teams per customer (lower than pure SaaS due to professional-services component); no deck figure
- Sales cycle / months to first revenue: 6–9 months for enterprise grocery
- Headcount / OpEx growth: scale with new logo additions; no deck data
- Current active locations: +300 locations
- Orders processed annually: >2 million
- AOV (from case study): $185 - useful as GMV proxy but NOT a revenue driver (no take-rate)
- Scenarios (Base / Bull / Bear - which variables flex):
- Base: 5–8 new logos/year, avg 15 stores/logo, moderate churn ~7%
- Bull: Accelerated US expansion, larger avg chain size (30+ stores/logo), churn <5%, new module upsell
- Bear: Slower enterprise sales cycles, higher churn (10%+), smaller avg store count, discount pressure
- Required sheets / outputs:
- Assumptions - all drivers with toggle for Base/Bull/Bear
- ARR Build - logo cohort table: new logos, avg stores, license fee, expansion, churn → net new ARR and ending ARR by month
- P&L - Revenue, COGS (hosting + dedicated dev team cost), Gross Profit, S&M, R&D, G&A, EBITDA
- Headcount - by function (Sales, Engineering, CS, G&A), tied to logo growth
- Cash Flow / Runway - net burn, ending cash (critical if raising)
- KPI Dashboard - ARR, logo count, store count, ARPU per store, gross margin %, net burn
Frequently asked
Is the Stor.ai financial model free?+
Yes. The Stor.ai 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 Stor.ai'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
Created by ex-finance professionals
Hey, I’m Alex and I created Finamodel.
Over my years in the finance industry I kept building the same models over and over again. Same structure, same assumptions, different logo. So I started building frameworks to turn them into clean, reusable templates.
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