AI Integration Services

AI inside the product you already ship

AI integration means putting a language model into a product or an internal tool you already run. The model answers from your knowledge, not from a general training set and a hope. Retrieval over your own knowledge base is the usual core: the product finds the relevant pages, records, or policies first, and the model writes from those.

This is for teams that already have a product, a help centre, a policy library, or an internal tool, and want AI in that surface. It is not a new app that your users have to open instead. The assistant, the suggestion, or the draft appears where the work already happens.

We also use this when a product needs to classify, summarise, or draft from material you control. The boundary is the same in each case. The model may use what retrieval returned. It may not fill a gap with a plausible sentence and present it as your policy.

Quality, privacy, and the knowledge base

Output quality is tested against questions your team writes, including the ones the knowledge base does not cover. A correct refusal is a passing test. A fluent answer that is not in the source is a failing test. We keep those tests with the project so a later change to the knowledge base can be checked again.

The knowledge base is yours. We agree what is in it, how fresh it has to be, and who can update it. Stale pages are a product problem, not a model problem, and the integration should show when a source is old if that matters to the answer.

GDPR and HIPAA requirements are part of the scope when the product touches personal or health data. We use role-based access, and we can deploy on your own infrastructure so prompts and records stay in your environment. We do not treat a vendor's default retention settings as a decision you have already made.

How an integration is scoped

The discovery call covers the surface where the AI will appear, the sources it may read, and the actions it may not take. A draft that a person sends is a different product from a reply that goes out on its own. We do not blur those.

You get a fixed scope, a timeline, and a price after that call. The build uses your real content. Weekly demos show answers against that content, including the questions that should come back empty. Launch includes the test set, documentation of the sources, and a handover your team can operate.

If the knowledge you need is not written down anywhere, retrieval will not create it. We will say that early. Some of that work belongs in a process change first, and some of it belongs in an agent that does a defined job rather than an assistant that answers open questions.

What we need from you

We need the product or tool the AI will sit in, the documents or records that count as the source of truth, and a list of real questions, including ones you do not want answered. We need someone who can say whether an answer is acceptable, not only whether it sounds fluent.

We need access limited to that source and that environment. A broader connection is not a shortcut. It is how answers start citing the wrong system.