AI MVP development for products where a model does the core work
One job for the model, judged by real users before production.
AI MVP development starts with the one job your product hands to a model. That might be answering from your documents, pulling fields out of a PDF, drafting a reply, or running a short chain of steps as an agent. We build that job into a small product people outside your team can use, score it against cases you help write, and you find out whether the output is good enough to sell before you pay for production.
The short answer
An AI MVP is a first version of a product whose core comes from a model: an assistant, an extraction step, an agent, or a generator. It ships the one model-backed job, an interface around it, a small evaluation set, and logs of every run, so real users can show whether the output earns a place in their work.
Model routing
What it does
- The single model-backed job your product is built around, working end to end.
- Answers drawn from the documents, records, or examples you supply, with the source shown.
- An evaluation set of your real cases, scored before each release.
- A record of every model call with its input, output, model, and cost.
- A simple interface for early users, with a button to flag a bad answer.
Best for
Founders and teams whose idea only works if a model does its central task well, and who want outside users to judge that before production is on the table.
What you provide
- Real examples of what your product will receive, and what a good result looks like for each.
- The documents or data the model should answer from.
- A handful of early users willing to try it and say where it falls short.
[HOW WE'D BUILD IT]
We pin down the one job the model has to do well
Choose what the model owns
We name the single task that decides whether the product works, write down what a passing result looks like, and park every other AI idea for later.
Score it on your cases
The model reads from the documents or data you hand over. We collect a few dozen real cases with expected answers and run every prompt or model change against them before a user sees it.
Read the run log
Early users work with it while we record each run and its cost. Flagged answers and the cost per run show whether the product holds up and what production would have to add.
What the model gets now and what it gets at scale
[TALK TO A BUILDER]
Bring the task you want a model to own
Describe what the model would read, what it would hand back, and who would rely on that answer. After a conversation about fit, paid discovery narrows it to the piece your MVP tests first.
Measured from the first release
Your cases become the evaluation set every release is scored against.
Runs you can inspect
Each call is kept with its input, its output, and what it cost.
Your code and prompts
The codebase, the prompts, and the evaluation set stay with you.
Room to grow
If users want it, monitoring and fallbacks go onto the same codebase.
[QUESTIONS]
Answered before you ask.
See where AI can save you time
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