# Stor.ai Financial Model B2B SaaS platform powering end-to-end digital commerce (engagement, merchandising, fulfillment) for grocery and retail chains. - Canonical: https://finamodel.com/startups/storai - Excel download: https://finamodel.com/startup-models/storai.xlsx - Category: AI/ML - Model type: SaaS ARR / Valuation - Funding round: Series A - Funding: $21M - Founded: 2021 - Geography: Global - US, Canada, Europe, GCC, Israel. HQ: Tel-Aviv and NYC. [DECK slide 10] - Customer: B2C ## About the company 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, $185 average order value, and 50% picking-labour reduction. The model follows retail logos, stores deployed, license fees, implementation ramp, expansion, and churn. It incorporates fulfilment-support and cloud costs, sales capacity, product and operational hiring, gross margin, cash burn, and runway, while treating order volume as an adoption and ROI driver rather than fee revenue. ## What's included - 5-year monthly revenue build with stage-appropriate growth assumptions - Full P&L, headcount plan, and operating-expense schedule - Cash-flow statement, runway, and burn-rate tracking - Valuation via exit multiple with a DCF cross-check - Returns analysis with MOIC and IRR - Unit economics including CAC, LTV, payback, and cohort retention ## Product & value proposition Four-pillar "Customer-First Commerce" platform for grocery retailers: 1. **Customer Engagement** - unified product browsing, discovery, and shopping UX across all digital touchpoints. 2. **Digital Merchandising** - brings trade-promo and shopper-marketing into the digital ecosystem; controls above-the-fold "prime real estate" in category/search results pages (84% engagement differential above vs. below fold, citing Nielsen Norman Group). 3. **Frictionless Shopping** - mobile app, Scan & Go, Smart Carts. 4. **Fulfillment** - three picking-mode app (Android/iOS) with POS integration, final-mile delivery partner integrations, MFC and dark-store capable. Delivers ~50% labor reduction for picking. ## Revenue model - **Per-store license fee** - recurring SaaS charge billed per retail location. - **No transaction fees** - explicitly stated, differentiating from GMV-take-rate models. - **ROI to customer typically <12 months** - used as a sales / conversion argument. - Specific fee amounts ($ 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**: 1. **Assumptions** - all drivers with toggle for Base/Bull/Bear 2. **ARR Build** - logo cohort table: new logos, avg stores, license fee, expansion, churn → net new ARR and ending ARR by month 3. **P&L** - Revenue, COGS (hosting + dedicated dev team cost), Gross Profit, S&M, R&D, G&A, EBITDA 4. **Headcount** - by function (Sales, Engineering, CS, G&A), tied to logo growth 5. **Cash Flow / Runway** - net burn, ending cash (critical if raising) 6. **KPI Dashboard** - ARR, logo count, store count, ARPU per store, gross margin %, net burn ## Frequently asked questions ### Is the Stor.ai financial model free? Yes. The Stor.ai model is a free Excel download with live formulas. ### Can I change the assumptions? Yes. The workbook is editable and its live formulas recalculate when assumptions change.