Shaping an AI product that people will use
Many AI ideas fail not because the model is weak but because the product is vague. We begin with the user, the job they are trying to finish, and the moment where AI changes the outcome. A good test is whether the product still helps when the model is right only most of the time, and whether the user can notice and correct a miss.
From there we define the smallest version that proves the value: one workflow, one type of user, a handful of real inputs. We prototype quickly with hosted models and run sessions with target users, so assumptions about trust, wording and acceptable mistakes are tested before engineering effort is committed. Even a clickable prototype with a hosted model is enough to see where users hesitate or over-trust.
