Recogni Financial Model
AI/ML Startup Financials (Free Excel Download)
Recogni builds a custom ASIC (VPU-1000) delivering 1,000 TOPS at 5W - the first chip to simultaneously optimise for both AI inference performance and power efficiency for Level 3+ autonomous vehicles.
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About this model
Recogni designs the VPU-1000, a custom AI-inference ASIC targeting Level 3-plus autonomous vehicles. Its Vision Computing Module combines the chip with sensor fusion, passive cooling, night vision, and a claimed 1,000 TOPS at five watts.
The fabless company sells into long automotive OEM and Tier-1 qualification cycles. It reports ongoing technical engagements and commitments to invest, plus seven provisional patents, but no contracts, shipments, revenue, or unit prices; production revenue follows design wins after a multi-year lag.
The model follows OEM pipeline, design wins, qualification milestones, vehicle production ramp, module ASP, and units shipped. It separates non-recurring engineering and R&D from manufacturing COGS, then tests yield, gross margin, working capital, cash burn, funding, 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 Recogni
recogni.com
How to build a detailed financial model for Recogni
A complete walkthrough of the business, drivers, and assumptions behind the downloadable Recogni model - distilled from its pitch deck and publicly available information.
Product & value proposition
- Product: Recogni VCM (Vision Computing Module) containing 1x VPU-1000 ASIC plus sensor fusion (infrared, monochromatic, colour, external depth cameras).
- Key specs: 1,000 TOPS at 5 Watts, passive cooling, 120 dB dynamic range, night vision.
- Competitive claim: <100W to process 95% of all AI workload for an entire vehicle; competition requires >26KW to achieve equivalent L3+ performance (7,000+ TOPS current SoA).
- Efficiency claim: >200× more power-efficient than other accelerators.
- Patent protection: 7+ provisional patents filed.
- Technology readiness: Ongoing technical engagements and "commitment to invest" from several automotive OEMs and a Tier-1 parts supplier - no signed contracts stated.
Market
- Level 2+ ADAS market (addressable by existing solutions): $2B (2022) → $4B (2024).
- Level 3+ AV AI processing market (Recogni's primary TAM): $16B (2025) → $45B (2030).
- Sub-segments at $16B 2025 level: Self-driving Cars $6B, Ride-hailing Taxis $10B.
- Claim: Recogni technology could also address Level 2+ market ($4B by 2024).
- Market acceleration claim: Recogni will accelerate the AV market timeline by at least 2 years.
Revenue model
- Inferred go-to-market: direct sales to automotive OEMs and Tier-1 parts suppliers (based on mention of "ongoing technical engagements").
- Hardware unit (VCM module) appears to be the initial product; potential for software/IP licensing not mentioned.
Traction & metrics
- No revenue, customer count, contract values, or unit shipment figures disclosed.
- Qualitative traction: "Ongoing technical engagements and commitment to invest from several automotive OEMs and Tier-1 parts supplier."
- Patent progress: 7+ provisional patents filed.
- No ARR, MRR, growth rates, or retention metrics in deck.
Competition / moat
- Competitors shown on performance vs. efficiency scatter plot:
| Company | Performance | Power | | -------- | ----------- | ------ | | Hailo | 40 TOPS | 15W | | (unnamed, likely Qualcomm/NXP) | 30 TOPS | 13W | | (unnamed) | 24 TOPS | 10W | | (unnamed) | 15 TOPS | 5W | | (unnamed) | 15 TOPS | 3W | | Syntiant | 5 TOPS | 1W | | (unnamed) | 4 TOPS | 2W | | Recogni VPU-1000 | 1,000 TOPS | 5W |
- All competitors plotted as "Inference Only"; Recogni claims full-stack ASIC.
- Moat: custom silicon (ASIC vs. GPU/FPGA), patent portfolio, claimed 2-year market timing advantage.
Team & funding ask / use of funds
- Founding team:
- RK Anand (CEO) - prior: OttoQ (Founder/CEO), Kumu (President/CEO), Xsigo (Founder/VP Eng), Juniper (EVP/GM)
- Eugene Feinberg (Technology/CTO) - prior: mPerpetuo (Founder/CTO), EyeFi (Founder/Architect), Cisco, Growth Networks
- Ashwini Choudhary (Products, Marketing, BD) - prior: mPerpetuo (Founder/COO), Ventiva (CEO), NetFortis (Founder/CEO), Aarohi Comm
- Gilles Backhus (AI) - prior: Lilium (Sensor Systems), Konux (AI Lead), Kumu, Gauss (ML Scientist)
- Valerie Chan (Operations) - prior: Forq (Founder/CEO), EyeFi (VP Ops), Warpia (VP Biz Dev), Faircom (VP Sales)
Recommended financial model
- Archetype + why: Hardware / semiconductor revenue model - specifically a chip unit-economics + design-win pipeline model. Recogni is a pre-revenue fabless ASIC company selling into automotive OEM qualification cycles. The right model tracks design-win pipeline → production ramp → per-unit ASP × volume × gross margin. Not a SaaS ARR or 3-statement operating model at this stage - the business is capex-light (fabless) but heavily R&D-weighted with a 3–5 year design-win-to-revenue lag typical of automotive silicon.
- Forecast horizon & granularity: 7 years (2019–2026), annual. Years 1–2 = R&D/pre-production (burn only); Year 3 = first design-win revenue; Years 4–7 = production ramp. Monthly granularity for cash runway in Year 1–2.
- Key drivers & assumptions:
- *Market size* - Level 3+ AI processing TAM: $16B in 2025, $45B in 2030; CAGR ~23% implied
- *Recogni market share* - 1–3% of L3+ TAM by 2030; automotive design cycles are long and OEM qualification is a major gating factor
- *ASP per VCM unit* - $500–$1,500 per module (automotive AI compute modules typically $200–$2,000 depending on performance tier; no deck data)
- *Units per vehicle* - 1–2 VCM modules per L3+ vehicle; L3+ minimal config = 8 cameras, 5 radar, 4 LiDAR
- *Gross margin* - 40–55% at scale (fabless semiconductor typical range); lower in early ramp due to NRE recovery and yield
- *NRE (Non-Recurring Engineering) revenue* - $1–5M per major OEM engagement in design phase (common in automotive silicon)
- *R&D opex* - largest cost line pre-revenue; tape-out costs for automotive-grade ASIC typically $5–30M
- *Design-win timeline* - 18–36 months from engagement to production sign-off (standard automotive)
- *Number of OEM design wins* - 2–3 initial OEM engagements based on "several OEMs"; convert 1 in base case by Year 4
- *Revenue recognition* - upon module delivery (hardware), or milestone-based for NRE contracts
- *Headcount ramp* - 20–40 engineers by end of Year 2; Silicon Valley/automotive comp rates
- *Burn rate* - $3–6M/year pre-production; highly R&D intensive
- *Tape-out / foundry costs* - capitalized or expensed depending on accounting treatment; major cash event
- Scenarios (Base / Bull / Bear - which variables flex):
- *Base:* 1 OEM design win by 2023, production ramp to ~50K units/year by 2026, 2% L3+ market share
- *Bull:* 2–3 OEM wins, faster Level 3+ market adoption, ASP at high end, NRE revenue from multiple Tier-1s
- *Bear:* No production win before 2025 (automotive qualification delays), L3+ market slower than projected, competitor catches up on efficiency, cash runway exhausted requiring bridge
- Required sheets / outputs:
- Assumptions dashboard (all drivers toggled by scenario)
- Design-win pipeline tracker (OEM engagement → NRE → production → volume ramp per customer)
- Revenue build: NRE revenue + unit volume × ASP by OEM
- P&L: Revenue, COGS (per-unit wafer + assembly + test), Gross Profit, R&D, S&M, G&A, EBITDA
- Cash burn & runway (monthly for Years 1–2; quarterly thereafter)
- Headcount plan
- Market context tab: TAM bridge from $16B (2025) to $45B (2030) with Recogni share overlay
- Sensitivity table: ASP × Volume, Margin × Design-win timing
Frequently asked
Is the Recogni financial model free?+
Yes. The Recogni 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 Recogni'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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