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AI & Intelligence
We build retrieval-augmented generation systems that answer from your own documents - with sources, permissions and quality you can measure - instead of from a model's memory.
When it fits
RAG is the right call when people need answers from knowledge that lives in your documents, tickets, wikis or databases, when that knowledge changes often, and when every answer has to be traceable to a source. It's how assistants stay current without retraining a model.
Documents, tables and pages cleaned, chunked and kept in sync as the source changes.
Vector, keyword or hybrid search with re-ranking, tuned on your real questions.
People only ever get answers drawn from content they're allowed to see.
Every answer links back to the passages it came from, so it can be checked.
A test set built from your own questions scores grounding and accuracy on every change.
Latency, cost per answer and quality tracked in production, with alerts.
How we build it
Which sources matter, who can see what, and the questions people actually ask.
A working assistant measured against an evaluation set from week one.
Sync, permissions, guardrails, caching and fallbacks around the prototype.
Feedback and evaluation keep answers accurate as content and models change.
Tools we use
FAQ
Usually RAG. It keeps answers current and traceable without retraining. Fine-tuning helps with tone, format or very specialised language, and the two can be combined.
Yes. Access rules are applied at retrieval time, so an answer is only ever built from documents the person asking is allowed to read.
Answers are constrained to retrieved sources and cite them, and an evaluation suite measures grounding on every change. When the sources don't contain an answer, it says so.
Let’s talk
Tell us what you're building. We'll come back with a clear plan, the right team and an honest timeline.