AI product engineering
AI inside a product people will use. The model, the validation layer, and the interface are designed together.
What this engagement is
Most AI projects fail in the gap between a fluent model and a product someone depends on. We close that gap. The work covers what the system is allowed to say, what it must refuse, and how a person can tell the difference.
We do not start by picking a model. We start with the decision the software is supposed to support, the cost of being wrong, and the data that is allowed to leave the device.
How we build it
A recommendation path gets a validation step before it ships. For Zealo, that step checks training guidance against sports-science constraints before it reaches an athlete. The same pattern applies to client products: sources, limits, and a way to say “we don’t know.”
The interface is part of the system. If the model is uncertain, the screen should look uncertain. Trust is a behavior, not a badge in the footer.