TOToch.ai Financial Model
Media/Gaming Startup Financials (Free Excel Download)
AI-powered real-time video clipping, metadata tagging, ad monetization, and cross-platform publishing platform for broadcasters and content owners.
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About this model
Tochai automates the post-production workflow for broadcasters, sports-rights holders, OTT platforms, and studios. It turns a process that can take one to three hours into one to three minutes through AI clip generation, metadata tagging, graphic overlays, compliance checking, and publishing to more than 30 platforms.
Customers control the workflow through a dashboard, can ingest from a streaming link without integration work, and receive outputs including Google Web Stories. The product claims more than 90% time savings and a 70% reduction in manual costs. These are product-value claims rather than customer or revenue traction.
The business model is implied enterprise SaaS, potentially priced by seats, channels, streams, clips, or destinations; no tiers, ACV, contracts, or customer metrics are disclosed. A model should therefore build subscriptions and usage separately, then include AI inference, storage, delivery, compliance, implementation, sales, and retention costs.
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 Toch.ai
toch.ai
How to build a detailed financial model for Toch.ai
A complete walkthrough of the business, drivers, and assumptions behind the downloadable Toch.ai model - distilled from its pitch deck and publicly available information.
Product & value proposition
- Replaces a 1–3 hour manual post-production workflow (download → edit → add ads/graphics → upload) with a 1–3 minute automated pipeline.
- Key capabilities: real-time AI clip generation, automated metadata tagging, one-click ad/graphic overlay insertion, compliance checking, cross-platform publishing to 30+ platforms, Google Web Stories output, no-integration ingest via streaming link.
- Customer retains full control via a dashboard with self-serve credentials.
- Claimed efficiency gains: 70% manual cost reduction, >90% time savings.
Market
- TAM: $210B - global digital video content ($170B) + digital video ad spend ($40B) in 2020 (IMARC group forecast).
- SAM: $10B - annual revenue opportunity from short-form video creation from live/pre-recorded content + monetization + compliance for live video/OTT.
- SOM: $1B - current focus on live sporting events and new TV show/movie production (stated as 10% of SAM).
- Market tailwind cited: 68% of consumers prefer video over text; 55% prefer videos <2 minutes; 70% prefer regional languages; 30-second ad format dominant (66% share Q3 2020, up from 55% in Q3 2019).
- No CAGR or growth rate given for the addressable market.
Revenue model
- Not explicitly stated in deck. Business model is implied B2B SaaS / platform fee.
- Customers appear to be broadcasters, sports rights holders, OTT platforms, TV/film studios - enterprise/mid-market.
- Likely SaaS subscription (per seat or per channel/stream) plus potentially usage-based (per clip or per output platform). Rationale: dashboard-credential delivery model and "entire control lies with the client" language implies tenant-based SaaS access.
- No pricing tiers, ACV, or contract details disclosed.
Traction & metrics
- No revenue, customer count, ARR, or growth figures in deck.
- Efficiency claims are value-proposition metrics, not traction: 70% cost reduction, >90% time savings.
- Market consumption data (slide 5) is third-party research, not company traction.
Competition / moat
- Competitive positioning shown on a 2×2: axes are Real-Time vs. Delayed (y) and Offline vs. Cloud Based (x).
- Toch positions itself top-right: Real-Time + Cloud Based - the only player in that quadrant.
- Named competitors:
- ClipMine - real-time but offline/on-prem
- WSC Sports - cloud-based but not fully real-time
- THE TAKE AI, Blink, Video++, Mirriad, minute. - all offline/delayed
- Moat claimed: real-time cloud AI processing; no competitor occupies the same quadrant.
- No patent, data, or switching-cost moat described.
Recommended financial model
- Archetype + why: B2B SaaS ARR model - the product is a dashboard/API delivered per client tenant; revenue will recur via subscription or usage-based contracts. A 3-statement operating forecast should sit underneath.
- Forecast horizon & granularity: 3 years monthly (Year 1–2) → quarterly (Year 3); aligns with likely Series A fundraise horizon and the early-stage customer ramp.
- Key drivers & assumptions:
- SOM addressable: $1B
- Market penetration rate: 0.1–0.5% of SOM by Year 3; rationale: early-stage, niche initial focus (live sports + TV production)
- New logos per quarter: 2–5 in Year 1, ramping to 10–20 by Year 3; rationale: enterprise sales cycle, no stated pipeline
- ACV per client: $50K–$150K/year; rationale: mid-market broadcaster segment, comparable to WSC Sports enterprise pricing range
- Revenue model split: 80% subscription / 20% usage overage; rationale: dashboard SaaS delivery model
- Gross margin: 65–75%; rationale: cloud-hosted AI inference has meaningful compute cost but no physical COGS
- Time savings claimed >90% and cost reduction 70% - use as customer ROI / value-based pricing anchor in assumptions
- Churn: 10% annual; rationale: enterprise broadcast contracts tend to be sticky but no retention data disclosed
- CAC: $20K–$40K per enterprise logo; rationale: direct/field sales required for broadcasters and sports rights holders
- LTV/CAC: target >3× at steady state; rationale: standard SaaS benchmark
- Headcount: 15–25 FTE in Year 1 scaling; rationale: seed/pre-Series A tech-heavy team typical for AI media-tech
- Opex: R&D ~40% of revenue, S&M ~30%, G&A ~15% in Year 1; normalising toward profitability by Year 3
- Scenarios (Base / Bull / Bear - which variables flex):
- Bull: faster logo ramp (20+ logos/quarter by Year 2), ACV at top of range ($150K), churn 5%
- Base: mid-range logo ramp, ACV $80K, churn 10%
- Bear: slow enterprise sales cycle (5 logos/quarter by Year 2), ACV $50K, churn 15%, compute costs compress margin
- Required sheets / outputs:
- Assumptions (all drivers, scenario toggle)
- Revenue build (logo count × ACV, MRR/ARR waterfall, NRR)
- P&L (gross margin, EBITDA bridge)
- Headcount plan
- Cash & runway (with fundraise milestones)
- Market sizing summary (TAM/SAM/SOM from deck)
- Dashboard (ARR, logo count, gross margin %, runway)
Frequently asked
Is the Toch.ai financial model free?+
Yes. The Toch.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 Toch.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
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