An AI feature that sounds right on paper can behave completely differently on your real data. This prototype tests it against the real thing, in a day or two — not a demo built on cherry-picked examples.
A regular software MVP mostly risks whether people want the thing. An AI MVP carries a second risk on top of that: whether the AI part actually holds up once it meets your real data, your real edge cases, and your real users — not the clean examples in a product demo. That gap is exactly what an AI MVP needs to close before anyone commits real budget to it.
So this prototype is built and tested against your actual data and your actual use case from day one, not a synthetic dataset chosen to make the demo look good. If the AI holds up, you know before you've built anything else around it. If it doesn't, you've found the real limitation in a day or two, not after the rest of the product was built on top of an assumption that didn't hold.
You find out how the AI actually behaves on the inputs it'll actually see.
A day or two, not a discovery phase that delays the decision it's supposed to inform.
The same team and pipeline that builds Document Intelligence, Conversational Voice, and the other engines running in production today.
Works, and it moves straight into the full AI SDLC. Doesn't, and you've spent a day or two finding that out — not a quarter.
A day or two is Altamira's target for this track, not a claim proven identically on every engagement. What's not a target: the Rapid PoC pipeline is live and running today, the same mechanism described on the main Rapid Prototyping page — a real, lighter track built for exactly this, not the full AI SDLC with steps quietly skipped.
You already know the AI works and just need the full product built — AI-Accelerated Software Delivery or AI Squads is the more direct path. Not sure AI is the right fix for what you're trying to do at all yet? Start with AI Transformation Advisory instead.