Service
AI integration
Language models inside your application: one narrow job at a time, with costs, limits and a way to switch it off.
Adding a language model to a product is an integration task, not a research project. The model is a remote service that is slow, occasionally unavailable and billed per token — treated like a payment gateway, it behaves predictably.
We start from a job that has a measurable outcome: summarising incoming requests, extracting fields from messages and documents, drafting descriptions for a catalogue, answering support questions from your own knowledge base, translating content into the languages of the site. Each of those can be shipped in days and judged on results.
How we build it:
— the API key stays on the server, never in the browser and never in the repository;
— the call runs in a queue, so nobody waits ten seconds for a page;
— timeouts, retries and a cache, so a slow or repeated request costs nothing extra;
— hard limits on tokens and on how much a single user can trigger;
— logging of usage and cost, so the bill is never a surprise;
— structured output validated by your code — a model's answer is user input, not truth.
And the part that is usually missing: a plan for when the model is wrong. Generated text arrives as a draft a human approves, the original is kept alongside it, and the feature degrades to the previous behaviour when the API is down. An integration nobody can turn off will eventually take the product with it.