What we'd build for you — A to Z.

What separates a use case that ships from one that stalls

The list above is deliberately long, because the useful question is rarely “can AI do this?” — it's almost always yes. The question is whether this particular version of it is worth building. Four things decide that, and none of them is the model.

The task repeats often enough to measure

A workflow that happens forty times a day generates enough signal to evaluate, tune, and prove value against. One that happens twice a month will take a year to tell you whether it worked. Volume is what makes a use case improvable rather than just automatable.

Someone can say what “correct” looks like

If no one on your team can look at an output and rule it right or wrong, there is no eval suite, and without one you are tuning by feel. The best candidates are tasks a person already does where a second person could check the work.

The data the answer depends on is reachable

This is where most promising use cases actually die. Not because the model can't reason, but because the policy it needs lives in a PDF nobody has opened since 2021, or in the head of one person in operations. Grounding is the constraint, not intelligence.

Being wrong is survivable

Drafting a reply someone approves is a good first build. Issuing a refund unsupervised is not — at least not until the drafting version has earned it. Start where the cost of a mistake is a rejected suggestion, and expand on evidence.

A pattern that clears all four is usually a 4–12 week build. One that clears two is worth a strategy engagement before anyone writes code.