Connecting language models to the software that runs your business

The engineering that surrounds a model

Calling a language model is a few lines of code. Everything that makes the call worth making is the part around it. Which information should be retrieved and sent. How to structure the request so the response is usable by software rather than only readable by a person. What to do when the service is slow, rate limited or briefly unavailable. How to keep costs predictable when volume grows. What gets recorded so that an output can be examined later.

We have been building that layer into production applications for several years. It is ordinary software engineering rather than anything exotic, and it is precisely what separates a feature that survives contact with real users from a prototype that impressed everybody in a meeting and was switched off two months later.

Retrieval, so answers come from your information

Most useful integrations depend on retrieval. Your documents and records are indexed so that, when a request arrives, the system finds the genuinely relevant material and includes it in the request to the model. The answer is then based on your information rather than on whatever the model absorbed from the internet during training.

Doing this well involves decisions that materially affect quality, including how documents are divided, how relevance is judged, how much context is worth including given the cost, and how to handle material that has been superseded. We also keep the source of every answer available, because a response that cannot be verified is difficult to act on with confidence.

Agents that carry out defined tasks

An agent is a model given the ability to take actions rather than only produce text, such as looking up a record, creating a draft, scheduling something or calling another service. Used carefully this is genuinely powerful. Used carelessly it is how automated systems do embarrassing things at scale.

Our approach is deliberately conservative. Each agent gets a specific, bounded task rather than general permission to act. The operations it may perform are enumerated explicitly, and anything irreversible or financially significant requires human confirmation. Every action is logged with what triggered it. Limits prevent runaway loops. The result is automation you can audit, which is the only kind most businesses should deploy.

Reliability, cost and privacy in production

Production integrations need engineering for the ordinary problems. External services occasionally slow down or return errors, so requests retry sensibly and the application degrades gracefully rather than showing users a broken page. Heavy work runs through Celery so nobody waits on a page for a model to finish. Responses that repeat get cached, which reduces both latency and cost.

On privacy, we are explicit with you about what data leaves your systems, which provider receives it and under what terms, and we design to send the minimum required rather than everything available. Where obligations are strict, that constraint shapes the architecture from the beginning, and we will tell you honestly when a requirement means a particular AI feature should not be built at all.