Skip to main content
K4M2 AI

Service

Enterprise knowledge search

Enterprise knowledge search lets people ask a question in plain language and get an answer drawn from your own documents, with a citation for every claim. The engineering that matters is not the model. It is retrieval quality, permission handling, and refusing to answer when the source material does not support one.

Who this is for

Organisations where the answer already exists somewhere, in a contract, a report, a policy, a ticket history, and finding it reliably takes an experienced person twenty minutes.

The business problem it addresses

Knowledge that exists but cannot be found is a cost that never appears on any budget line. It shows up as slow onboarding, inconsistent answers to customers, and decisions made on the version of a document someone happened to have.

A general chatbot bolted onto a document store makes this worse, because it produces confident answers with no way to check them.

Signs you may need this

Two people give different answers to the same policy question. New joiners take months to become useful. Experts spend their time answering the same question. Nobody trusts search enough to use it.

What the engagement includes

Source inventory and access mapping. Chunking and indexing strategy tested against real questions. Permission-aware retrieval so people only see what they are entitled to. Citation and receipts on every answer. An evaluation set of real questions with reviewer-agreed answers. Refusal behaviour when evidence is thin.

How quality is measured

On a set of questions your own experts have answered, scored for whether the answer is correct, whether the citation supports it, and whether the system correctly declined when it should have. Retrieval is measured separately from generation, because they fail differently.

When this is not appropriate

When the source material is contradictory and nobody will decide which version governs. When access rules cannot be established. When the real problem is that the documents are wrong, in which case a search system just distributes the error faster.

Questions buyers ask

Can sensitive data stay in our infrastructure?

Yes. Deployment is chosen against your security requirements, whether that is your cloud, your infrastructure, or an approved provider.

How do you stop it inventing answers?

Grounding every claim in retrieved source text, showing the citation, and designing refusal as a correct outcome rather than a failure.

Does it respect our existing permissions?

Retrieval is permission-aware by design. A person should never see through this system what they could not open directly.

What about documents that change?

Re-indexing is part of the system, not a manual task. Stale answers are treated as defects.

Where to go next

See all AI consulting services →Read why context, not model quality, decides reliability →See how retrieval receipts work →Read about model and system evaluation →Read about AI implementation for mid-market enterprises →
Start a conversation