AI product development

What this looks like

Most AI projects stall between a promising demo and something you can put in front of customers. The gap is rarely the model. It’s the unglamorous parts: what happens when the model is unavailable or wrong, how output is structured so the UI can rely on it, what data is allowed to leave the device, and how you’ll know next month whether quality has slipped.

We start by finding the one or two places where a model removes real work for your users, then build a working prototype against your actual data. If it earns its place, we harden it: typed outputs, fallbacks, evaluation sets and monitoring.

Where we’ve done it

journeybot generates packing lists and weather summaries with Apple’s on-device Foundation Models, so trip plans never leave the phone. We’ve written about what that takes in practice.

Good fit if

• You have a product and a hunch that AI could remove a step from it.
• Privacy or cost rules out sending everything to a hosted model.
• You’ve tried a prototype and it’s unreliable in ways that are hard to pin down.