Octi Financial Model
AI/ML Startup Financials (Free Excel Download)
AR social app that uses on-device human-body AI to let users scan people and surface their curated digital profiles and content.
professionals from Deloitte
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About this model
Octi is a consumer AR social app that uses on-device human-body AI to recognise people and surface curated profiles and content. Its patented technology includes facial recognition, segmentation, skeleton detection, mesh reconstruction, and gesture recognition running on a smartphone GPU.
The product frames people as a platform for persistent identity, effects, stickers, holographic messages, games, and integrations with Spotify, Instagram, Yelp, and Venmo. Brand Mini Apps, including a Taco Bell example, introduce sponsored AR and commerce opportunities.
The model begins with user growth, engagement, retention, and brand-partnership revenue rather than subscriptions. It tracks active users, campaign volume and pricing, potential API licensing, content and infrastructure costs, partnership sales, product hiring, cash burn, and runway. The deck reported 63,000 faces scanned by 1,200 people in two weeks.
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 Octi
octi.tv
How to build a detailed financial model for Octi
A complete walkthrough of the business, drivers, and assumptions behind the downloadable Octi model - distilled from its pitch deck and publicly available information.
Product & value proposition
- Core product: Mobile app (iOS/Android) that uses the smartphone camera to recognise people in real time, surface their profile, and overlay AR effects/content.
- Key tech (patented): Facial Feature Recognition, Instance Segmentation, 2D/3D Skeleton Detection, Volume Mesh Reconstruction, Gesture Recognition - implemented at GPU level (Metal / Vulkan).
- Experiences: Persistent Identity & Effects, Person Stickers, Dittos, Body Suits, Holographic Messages, Gesture Games & Animations.
- "Humans as a Platform" framing: the user's body/identity becomes a node connecting third-party services (Spotify, Instagram, Snapchat, Yelp, Venmo).
- Mini Apps layer: brand/commerce integrations shown (Taco Bell free taco, Spotify music search, community donation campaign).
Market
- Qualitative framing: AR described as "the next big tech platform / Mirror World".
- No TAM/SAM/SOM figures, market size numbers, or growth rates are stated in the deck.
Revenue model
- Not explicitly stated. Implied monetisation vectors from product slides:
- Brand Mini Apps / sponsored AR experiences (Taco Bell example) - B2B brand/advertising revenue.
- Platform / API licensing to third-party services (Spotify, Yelp, Venmo integrations).
- No subscription, IAP, or direct consumer pricing shown in deck.
Traction & metrics
- 63,000 faces scanned by 1,200 people in 2 weeks.
- No revenue, DAU/MAU, download counts, retention rates, or growth percentages shown.
Competition / moat
- Problem framed as: existing social platforms fail on virtual self-representation; avatars have no utility; consumer AR has had no breakout moment.
- Competitive positioning attributes: "No Utility / Pure Novelty / Low Engagement / Zero Retention" applied to incumbents.
- Moat: patented on-device human-understanding tech (GPU-level custom programming); first-party identity graph.
- No explicit competitive matrix or named competitors shown.
- Social proof / validators: Tom Conrad (fmr VP Product, Snap): "impressive suite of capabilities… wide range of compelling applications"; Joe Marchese (President Ad Revenues, Fox): "will change how people can communicate using AR".
Team & funding ask / use of funds
- Team: Not shown (no founder/team slide in deck).
- Investors / advisors named:
- Scott Belsky (CPO Adobe)
- Joshua Kushner (Founder, Thrive Capital)
- Shasta Ventures
- Tom Conrad (fmr VP Product, Snap)
- Shiva Rajaraman (CPO, WeWork)
- Abbe Raven (CEO, A&E Networks)
- Bold Capital Partners
- LiveNation
- Joe Marchese (Founder, Human Ventures)
- NFLPA One Collective
- I2BF Global Ventures
Recommended financial model
- Archetype + why: Consumer social / brand advertising platform model. The product is a free consumer app; the only revenue signal in the deck is B2B brand Mini Apps (sponsored AR, Taco Bell example) and potential platform API licensing. This is closest to a consumer app + ad/partnership revenue model - similar to early Snapchat or TikTok monetisation. A full 3-statement model is premature given minimal traction data; a user-growth + revenue bridge model with a brand/partnership revenue line is most appropriate.
- Forecast horizon & granularity: 3 years (2019–2021), monthly for Year 1, quarterly for Years 2–3. Given near-zero disclosed traction, Year 1 is effectively a launch ramp.
- Key drivers & assumptions:
| Driver | Value / Source |
|---|---|
| Beta users at close of 2-week pilot | 1,200 |
| Faces scanned per user in 2 weeks | ~52.5 (63,000 / 1,200) |
| Monthly active user (MAU) growth rate | 15–25% MoM in early growth phase; slows to 5–8% MoM by Year 2; typical early consumer social S-curve |
| DAU/MAU ratio | 20–30%; consumer social benchmark; no retention data in deck |
| Brand Mini App deals - average deal size | $25k–$100k per brand campaign; comparable to early Snapchat sponsored lens pricing |
| Brand deals per quarter (ramp) | 0–1 in Year 1, 2–4 in Year 2, 5–10 in Year 3; gated by user scale |
| Platform/API revenue | small or zero in Year 1; include as optional toggle in Year 2+ |
| COGS (cloud infra, on-device model serving) | 20–35% of revenue; AR/CV inference is compute-intensive |
| R&D headcount | core AI/CV team of 5–10 engineers; largest opex line |
| S&M (user acquisition) | primarily organic / influencer at launch; paid UA budget modelled as % of funding raised (unknown - flag as open question) |
| G&A | 10–15% of total opex |
| Funding raised | unknown - key variable; model should parameterise runway from raise size |
- Scenarios (Base / Bull / Bear - which variables flex):
- Base: MAU growth 15% MoM, 2 brand deals/quarter by Q3 2020, $40k avg deal.
- Bull: MAU growth 25% MoM driven by viral AR moment; 5+ brand deals/quarter, $75k avg; platform licensing kicks in Year 2.
- Bear: Growth stalls at ~5,000–10,000 MAU; brand deals delayed to Year 2; runway < 18 months without follow-on.
- Flex variables: MAU growth rate, DAU/MAU, brand deal volume and ASP, fundraise amount.
- Required sheets / outputs:
- Assumptions dashboard (all drivers in one place, toggle Base/Bull/Bear)
- User funnel (downloads → registered users → MAU → DAU)
- Revenue build (brand Mini App campaigns: deals × ASP; platform/API licensing stub)
- P&L (Revenue, COGS, Gross Profit, R&D, S&M, G&A, EBITDA, Net Loss)
- Headcount plan (by function: Engineering/AI, Product, Sales/BD, G&A)
- Cash & runway (starting cash = fundraise amount; monthly burn; months of runway)
- Scenario summary (side-by-side Base / Bull / Bear on key KPIs)
Frequently asked
Is the Octi financial model free?+
Yes. The Octi 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 Octi'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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