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LLMs grounded in your own content

Retrieval-augmented generation, assistants and content tools built on your documents — with citations, guardrails and a bill you can predict.

A model that knows nothing about your business

A general-purpose model knows a great deal about the world and nothing about your business. Ask it a question about your own policy and it will answer confidently and wrongly.

Retrieval-augmented generation fixes that: the model answers from your documents and cites which one. We build these with the boring parts done properly — chunking that respects structure, evaluation against questions you supply, refusal when the answer is not in the corpus, and per-query cost you can see before you commit.

What the work includes

What the work includes

Corpus preparation

Your documents cleaned, chunked and embedded in a way that respects how they are structured.

Retrieval pipeline

Vector and keyword search combined, tuned on questions your team actually asks.

Grounded answers with citations

Every answer points at its source, so a person can check it in one click.

Guardrails

Refusal when the answer is not in the corpus. A confident invention is worse than "I do not know".

Evaluation set

Questions and expected answers you own, so changes can be measured rather than eyeballed.

Cost controls

Caching, model routing and per-tenant limits, with the running cost estimated up front.

Who this is for

If one of these sounds like your situation, it is worth a conversation.

Knowledge locked in documents

Policies, manuals and contracts that people search by asking a colleague.

Support answering the same questions

A large share of tickets already have an answer written down somewhere.

A chatbot that invents things

Built on a raw model with no grounding. It is fixable, and the fix is retrieval.

How we keep it honest

The difference between a model that helps and one that quietly misleads.

A baseline before a model

We measure how well the current process does first. Surprisingly often it is the thing to beat, and sometimes it wins.

Evaluated on data it has never seen

Held-out, time-split where time matters. An accuracy figure from the training set is not a result.

A human in the loop where it counts

Confident cases go through; uncertain ones go to a person. That boundary is set by you, not by us.

Monitored after launch

Data drifts, behaviour changes, accuracy decays. You get the alerting, not just the model.

Part of AI & Data

Part of AI & Data

AI that survives contact with your data

Machine learning, generative AI, automation and analytics — applied to problems where the numbers justify it, and measured after launch.

See all AI & Data services
What we commit to

What we commit to

  • A written recommendation not to proceed, where that is the honest answer
  • Accuracy reported on held-out data, with the failure cases shown
  • Your data stays yours — never used to train anything we sell elsewhere
  • A documented fallback for when the model is unavailable or unsure
  • Running costs estimated before you commit, not discovered on the first bill
How an AI project runs

How an AI project runs

Deliberately front-loaded with the question of whether to proceed at all.

Feasibility

We look at your actual data, establish the baseline, and give you a straight answer on whether this is worth building. Some of these end in "do not".

Prototype

A working model measured against the baseline on held-out data, with the error cases in front of you rather than averaged away.

Production

Integration into the system where the decision is actually made, with the human-review boundary, logging and rollback in place.

Monitoring

Drift detection, periodic re-evaluation, and retraining when the numbers say so rather than on a calendar.

Common questions

Because it has never seen them. Retrieval-augmented generation fixes that — the model answers from your own documents and cites which one, and refuses when the answer is not there.

Yes, and that is the common case: policies, manuals and contracts your team currently searches by asking a colleague. The documents stay yours and are not used to train anything.

Grounding, citations and an explicit refusal path. An assistant that says 'I do not know' is far more useful than one that invents a confident answer.

Is this the right fit?

Tell us what you are trying to do. If generative AI is not the answer, we will say so.

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