A complete Seed financial model for Artificial Intelligence startups. Revenue model, unit economics, hiring plan, cash flow projections, and funding scenarios — structured for investor review.
Projection Horizon
3 years (monthly for Year 1, quarterly for Years 2-3)
Model Tabs
7 core tabs
Format
Excel + Google Sheets
Path to Series A metrics and the unit economics that prove the business model. Seed investors model the path from current to Series A-level KPIs.
AI models must separate training cost (capital expense, amortized) from inference cost (variable COGS). Investors expect inference gross margin to improve as you scale. Show the gross margin at 2x and 10x current volume.
Usage-based or hybrid pricing model with API call volume, enterprise seats, or outcome-based fees. Model compute costs separately as a variable COGS line that improves with scale.
Seed models should have a clearly documented assumption page. Every assumption should include a source (comparable company benchmark, customer interview data, or market research). Avoid top-down market share assumptions.
Show base case (on-plan), downside (50% of plan), and recovery timeline from downside. Include a Series A readiness milestone tracker showing the KPIs required to raise.
A Seed Artificial Intelligence financial model should cover 3 years (monthly for Year 1, quarterly for Years 2-3) of projections with these tabs: Assumptions Dashboard, Revenue Cohort Model, Unit Economics, Headcount Plan, P&L Summary, Cash Flow Forecast, Series A Bridge. Path to Series A metrics and the unit economics that prove the business model. Seed investors model the path from current to Series A-level KPIs.
Usage-based or hybrid pricing model with API call volume, enterprise seats, or outcome-based fees. Model compute costs separately as a variable COGS line that improves with scale. The key revenue drivers are: API call volume x price per token or call; Enterprise subscription seats x ACV; Revenue share on outcomes achieved (if applicable); Professional services and implementation fees.
Artificial Intelligence unit economics at the Seed stage should include: Gross margin per API call at scale; Compute cost per inference (target: improve 20% QoQ); Enterprise deal CAC and payback period; Token/usage consumption growth by customer cohort. AI models must separate training cost (capital expense, amortized) from inference cost (variable COGS). Investors expect inference gross margin to improve as you scale. Show the gross margin at 2x and 10x current volume.
Seed models should have a clearly documented assumption page. Every assumption should include a source (comparable company benchmark, customer interview data, or market research). Avoid top-down market share assumptions. Start with the smallest unit of your business (one customer, one transaction, one seat) and build up from there. Every assumption should have a source or benchmark you can defend in an investor meeting.
Show base case (on-plan), downside (50% of plan), and recovery timeline from downside. Include a Series A readiness milestone tracker showing the KPIs required to raise.
Get the Artificial Intelligence Seed financial model as a pre-built Excel and Google Sheets template. Assumptions dashboard, revenue model, unit economics, and cash flow — ready to customize.
Includes Excel file, Google Sheets version, and model documentation guide