An AI MVP needs to prove two things at once — that people want the product, and that the model behind it actually works. We build the smallest version that proves both, with real usage data and model accuracy metrics you can put in front of investors.
A prototype built on a no-code AI builder demos well but rarely survives contact with real users or due diligence.
The AI is actually built into the product's core logic, not a thin layer over a no-code AI tool that can't scale.
Scoped to the smallest version that proves the core hypothesis, not a full product build.
Tracks the metrics that matter for the next fundraising conversation or pivot decision.
Reports real accuracy and confidence numbers, not just a demo that happens to work on cherry-picked inputs.
The MVP's architecture carries forward into the full product instead of becoming disposable prototype code.
No dependency on a no-code platform's pricing or limitations once you're ready to scale.
Focused on proving the core AI hypothesis with real users, fast.
The single AI capability that is the actual product bet, built to work on real data.
Just enough surrounding product to let real users interact with the AI feature meaningfully.
Tracks user growth, retention, and engagement from day one.
Measures how often the AI gets it right on real usage, not synthetic test cases.
Collects user feedback directly in-product to guide the next iteration.
Metrics packaged in a form you can present in a pitch deck or diligence room.
No-code AI builder platforms get a demo running fast, but they hit a wall quickly on real data volume, custom model behavior, and investor due diligence questions about how the AI actually works. A custom MVP is built to survive both real usage and scrutiny.
As small as the single AI feature that proves your core hypothesis, plus the minimum product around it needed for real users to try it meaningfully.
No — we architect the MVP so it can be extended into the full product, rather than treating it as disposable.
Most AI MVPs launch in 6-10 weeks, depending on how much custom model work versus off-the-shelf AI infrastructure the core feature needs.
Tell us the core hypothesis you need to prove — we'll scope the smallest AI MVP that actually tests it.