The Frameworks Behind StartupAI
We didn't invent a new methodology. We implemented the best existing ones and added what the books can't: automated evidence tracking, fit scoring, and structural separation of hypothesis from evidence.
Value Proposition Canvas
Osterwalder, Pigneur, Bernarda & Smith
The Value Proposition Canvas maps your customer's jobs, pains, and gains against your product's pain relievers and gain creators. When these sides align, you have product-market fit.
Most teams fill this in once on a whiteboard and never revisit it. StartupAI keeps the canvas alive: AI populates it from market research, scores the fit between customer profile and value map, and flags misalignments.
Customer Job (Hypothesis)
“Manage invoices quickly”
Customer Job (Evidenced)
“Manage invoices quickly”
3 sources · 0.72 fit
Testing Business Ideas
Bland & Osterwalder
Testing Business Ideas provides the evidence hierarchy: how to design experiments that move you from “I think” to “I know.” It categorizes evidence by strength — from desk research (weakest) to revenue generation (strongest).
StartupAI implements this hierarchy at the data model level. Every piece of evidence has a type and strength. Assumptions without evidence are flagged. The system helps you design experiments to test your riskiest assumptions first.
Evidence Strength (Weak → Strong)
Desk Research
Market reports, competitor analysis
Customer Says
Interviews, surveys (what they say they'll do)
Customer Does
Landing page signups, pre-orders
Revenue
Actual payment, usage data
What StartupAI Adds
- Automatic evidence categorization by strength
- Assumption prioritization by risk and uncertainty
- Experiment design suggestions for untested assumptions
- Fit score algorithm across the entire canvas
The Mom Test
Rob Fitzpatrick
The Mom Test teaches you how to ask questions that even your mother can't give you false positives on. The core insight: ask about their life and past behavior, not your idea and future hypotheticals.
StartupAI applies this principle when helping you design customer interviews. It flags leading questions, suggests alternatives grounded in past behavior, and categorizes responses by whether they reflect what people say vs. what they actually do.
Bad Question (Leading)
“Would you use an app that helps you manage invoices faster?”
Asks about a hypothetical future. Everyone says yes.
Good Question (Mom Test)
“Walk me through the last time you sent an invoice. What was frustrating?”
Asks about past behavior. Produces real evidence.
What We Add to the Books
These frameworks are powerful on paper. We make them operational.
Structural Separation
Hypothesis and evidence are stored as different data types, not just different colors on a sticky note.
HITL Checkpoints
AI generates analysis, but you approve every critical decision. No black box validation.
Fit Score Algorithm
Quantitative scoring of how well your value proposition fits customer needs, updated as evidence accumulates.
Source Attribution
Every claim traces back to its source. No hallucinated market sizes or fabricated competitor data.
StartupAI implements frameworks from Value Proposition Design (Osterwalder & Pigneur), Testing Business Ideas (Bland & Osterwalder), and The Mom Test (Fitzpatrick). StartupAI is not affiliated with or endorsed by these authors.
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