Decide Your AI Feature Before You Fund the Build

No, and we say so in writing. The prototype is production-shaped, meaning it behaves like the real thing on real-looking data, but it is built for testing and decision-making. If the answer is go, the design system foundation and the readout are what your engineers, or ours with a partner, build from.

No. A prototype can test usability, desirability, feasibility and the model's behaviour, and it can produce a much better investment decision. Product-market fit needs repeatable market behaviour over time: adoption, retention, willingness to pay. The sprint gets you to a confident decision about whether to find out.

We work with sanitised or synthetic data by default, which covers most of what the sprint needs. Where the model's behaviour on real data matters to the decision, we agree a controlled way to test it with your team and your compliance people. We have designed products that operate under HIPAA, OSHA and financial compliance requirements, and one of those is a case study on this site.

One workflow with a clear user job and a metric: an assistant inside an existing flow, a document or transaction review step, a summarisation or classification feature, an agent that takes a bounded action with human approval. "Add AI to the product" is not a sprint; we will help you pick the one workflow that is.

Then the sprint did its job. You get the evidence, the reasons, and usually a smaller adjacent opportunity that the sessions surfaced. A stop after three weeks is far cheaper than a stop after a quarter of engineering and a launch.

A classic design sprint produces a clickable prototype and a round of user feedback in five days. This sprint adds what AI features specifically need: a working prototype that calls a model, an evaluation baseline for its behaviour, the design of trust and failure states, and thresholds for a decision agreed before the work starts.

A senior product designer who builds the prototype in code, a researcher who runs and synthesises the sessions, and Masterly's founder on the brief and the readout. For features where model evaluation needs deeper engineering, we bring a technical evaluation partner and say so in the proposal.

It is a fixed-scope quote per feature, agreed after the intro call. If you want to see how we work before committing, start with a Trial Week; its fee is credited toward the sprint.