AI features that reach production.

Why this keeps happening

LLMs are being applied where a rule or a query would do, because the demo is more impressive. Prototypes ship without an evaluation set, so nobody can say whether the new prompt is better than the old one. And protected data gets pasted into models nobody has vetted, because the pilot was “just a test” until it was not.

Language models are in production at most companies now, and the two worries are the same everywhere: the data going in and what comes out. The result is a feature that works in the demo, cannot be measured in production, and has a compliance problem waiting in the logs.

How we work on it

Data path first. Before a prompt is written, we draw where the data comes from, which model sees it, where the output goes, and who approved each hop. That drawing becomes the written policy that ships with the feature.

An evaluation set and a cost figure for every feature. Fifty labeled examples and a weekly accuracy number turn “the assistant seems better” into a decision. The cost per thousand requests is on the same page, because a feature that works and costs more than it saves is still a failed feature.

Private hosting where it matters. For regulated data, the model runs inside your network or inside a vendor boundary your compliance team has signed.

And we will tell you when AI is the wrong tool. Often a rules engine, a regression, or a search index does the job for far less. Saying so is part of the service.