A fashion venture's AI product architecture.

The brief

A fashion venture wanted to turn several separate AI capabilities into one product: helping shoppers see what fits before they buy, so fewer purchases come back. Before committing budget, they needed to know whether it could actually be built, and what to build first.

What we designed

An architecture that combines proven models into a single product. Existing AI APIs and models handle the parts that are already solved well, a custom layer adds what they miss, and machine learning tuned on the venture’s own data makes the output specific to their catalog and their customers. A feedback loop keeps it sharpening: what shoppers keep, return, and rate feeds back as training signal.

What makes it hold up

We validated each component on its own, from the vision models to the custom layer, so every piece of the design is proven rather than assumed. We chose which models to rent and which to run. And the design stays swappable: a better model slots in without a rebuild, which matters in a field where the best option changes by the quarter.

Where it landed

The venture got a phased architecture it can commit budget against: validated piece by piece, honest about what is already solved and what is custom, and built to evolve as the technology does. This engagement was design and validation work; the build is the venture’s to sequence.