AI MVP Development
An AI MVP has a different central question from an ordinary one. The usual MVP asks whether people want this. An AI MVP usually has to answer whether the model is accurate enough on your real data for anyone to rely on it.
Discuss your projectBuild the evaluation before the product
This is the one piece of advice on this page that matters most. Before building the feature, build the means of measuring whether it works: a set of real examples from your actual data with the correct answers attached, and a harness that runs the system against them and reports accuracy.
Without that, you are judging by impression. Someone tries a few inputs, it seems good, and the project proceeds on a feeling. Then a change to a prompt or a model version makes it quietly worse on a category nobody tested, and there is no way to notice. Teams in this position spend months adjusting prompts without knowing whether they are improving anything, which is the most common way an AI project consumes budget without converging.
A hundred or two hundred labelled examples is enough to be genuinely useful, and assembling them takes a day or two of someone's attention. That is a trivial cost against what it protects. It also gives you something to put in front of a board or an investor that is not a demo — a measured accuracy figure on your own data, with the failure categories named.
The other reason to do it first: it frequently answers the viability question before you have built anything. If accuracy on your real documents is far below what the workflow needs, you have learned that in week two for very little money, which is exactly what an MVP is for.
What an AI MVP includes
An evaluation set and harness
Real examples with correct answers, run automatically, reporting accuracy per category. The first thing built and the thing every later change is measured against.
The core AI capability
Extraction, retrieval, classification, generation — whichever the product depends on, built against your real data rather than a clean sample.
A confidence and routing mechanism
Because the honest answer to "is it accurate enough" is usually "on most inputs". Uncertain cases route to a person rather than being guessed, which is what makes the product usable at less than perfect accuracy.
A human review interface
Where low-confidence output is confirmed or corrected. Doubles as your source of new evaluation examples, so the system improves from use.
Cost instrumentation
Model usage per request and per outcome, because AI unit economics can be the thing that makes an otherwise good product unviable, and you want to know early.
The surrounding product
Auth, the workflow, the destination the output goes to. Deliberately thin, because the uncertainty is not here.
Decisions worth making deliberately
- Which model, assessed against your evaluation set rather than against benchmarks. Benchmarks measure general capability; your task is specific and the ranking frequently differs.
- Whether retrieval or fine-tuning is the right tool — RAG for facts from your documents, fine-tuning for behaviour and format. Confusing the two is a common and expensive mistake, covered on our RAG page.
- What accuracy the workflow actually needs, which depends on the cost of an error. Spotting a document for review needs far less accuracy than posting a financial transaction.
- Whether data may leave your infrastructure, which constrains model choice and should be settled before building rather than after.
- Cost per unit of work at the volume you expect, modelled rather than assumed — this is where AI products most often fail commercially.
- What the product does when the model is wrong, which is a product design question rather than a machine learning one and is usually the difference between a usable tool and an unusable one.
Common questions
Related in Dedicated Development Teams
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