Custom AI Development

Demos are easy. Tuesday is hard.

Almost any modern model will produce something impressive on a curated example. The difficulty has moved: it is now in the unglamorous parts. What happens on the inputs nobody anticipated, what it costs at real volume, how you notice when quality slips, and who fixes it at 2am.

So we build in the order that surfaces those problems early rather than late. The boring infrastructure comes first, the model choice comes surprisingly late, and nothing ships without something to compare it against.

How a build runs

1. Establish the baseline

   Before any model, we measure how well the current process performs. Without this number you cannot tell improvement from enthusiasm, and a startling number of projects never establish it.

2. Build the evaluation first

   A test set drawn from your real data, including the awkward cases, with an agreed metric. It becomes the thing we optimise against instead of optimising against our own impressions.

3. Ship a thin slice

   One narrow path, working end to end, in front of real users early. A narrow system in production teaches you more in a fortnight than a broad prototype teaches in a quarter.

4. Harden it

   Rate limits, fallbacks, cost controls, sensible behaviour when the model is wrong or the provider is down. Data handling that a GDPR review will survive, designed in rather than retrofitted.

5. Instrument and hand over

   Monitoring on quality and cost, not just uptime. Documentation written for the engineer who inherits it, and enough pairing that your team owns the system rather than renting it from us.

if we can't measure it, we won't claim it