LLM & Generative AI Integration

A model that has never seen your data will answer confidently and get it wrong. The fix is to look things up first: we index your documents, tickets, records, or catalog, find the passages that bear on the question, and give the model only those, with links back to the source.

Most of the work is in the details. Splitting documents so a table is not cut in half. Combining keyword search with meaning-based search, because exact part numbers and names still matter. Ordering results so the best one comes first. And applying your permissions during the search, so nobody sees a passage from a document they could not open themselves.

Where we can, we build on infrastructure you already pay for — your existing Postgres database or search cluster — instead of adding another vendor to your stack.

An agent is a model that can call your functions. That is powerful, and it is also the fastest way to build something unpredictable. So we keep agents narrow: a defined set of tools, explicit permissions on each, and a human approval step wherever an action is expensive or hard to undo.

The useful agents we ship are unremarkable to describe and valuable to run — sorting an inbound queue and routing it with a reason, reconciling two systems that disagree, drafting a quote from a specification, or running a back-office process that used to mean copying values between four screens.

Two rules do most of the safety work. Credentials never go into a prompt: the model names the workflow and its parameters, and the server adds the address and the access token. And any code the model writes runs in an isolated sandbox with only the access it needs.

Every tool call is logged with its inputs and its result, so when something goes wrong you can see exactly what the model did.

A 300-page contract does not belong in a prompt. It is slow, expensive, and stops working entirely once several documents are involved. Instead we store the documents, process them in the background with OCR and table extraction, and let the model read only the parts it needs.

Document size then stops driving cost and speed. A contract review looks at the clauses that matter, and the answer cites a real page and paragraph, so someone can check it.

We work with the major providers — Anthropic, OpenAI, and open-weight models you can host yourself when data residency or cost calls for it. Providers differ in real ways, so we do not pretend they are interchangeable. What we do promise is that switching one means changing configuration and re-running the test set, not a rewrite.

Cost is a design input from the first week, not something you discover on the first invoice.

For teams in the EU, we design for where the data goes: regional endpoints, zero-retention settings, or fully self-hosted models when nothing may leave your infrastructure.

The first engagement is usually two to four weeks: discovery, a hard look at the data you would be searching over, and a working slice of the highest-value use case running against your real systems. That slice is the estimate. It tells you what the full build costs far better than a document does.

From there our CEO and senior engineers scope the delivery, and we assemble the team from a network of 100+ engineers we have worked with for a decade or more. Weekly demos, code in your repositories, production handoff, and post-launch support. In twenty years we have not abandoned a project.