A complete Pre-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
18 months
Model Tabs
5 core tabs
Format
Excel + Google Sheets
Cash runway, burn rate, and the key milestones that unlock your next round. Pre-seed investors focus on whether you have enough runway to prove the thesis.
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.
Build every assumption from first principles. Pre-seed investors will ask "how did you get to this number?" for every major line. Have a clear answer that ties back to market research or comparable benchmarks.
Model two scenarios: (1) raising your target amount, (2) raising 70% of target. Show what milestones you hit in each case and when you need to start the next raise.
A Pre-Seed Artificial Intelligence financial model should cover 18 months of projections with these tabs: Assumptions Dashboard, Revenue Model (monthly), Headcount Plan, Cash Flow Forecast, Runway Sensitivity. Cash runway, burn rate, and the key milestones that unlock your next round. Pre-seed investors focus on whether you have enough runway to prove the thesis.
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 Pre-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.
Build every assumption from first principles. Pre-seed investors will ask "how did you get to this number?" for every major line. Have a clear answer that ties back to market research or comparable benchmarks. 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.
Model two scenarios: (1) raising your target amount, (2) raising 70% of target. Show what milestones you hit in each case and when you need to start the next raise.
Get the Artificial Intelligence Pre-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