A retrieval assistant running in your own environment, answering real questions against your own documents, with a measured accuracy score on your own test set — not a vendor benchmark, not a demo on sample data.

Book a discovery call →See the ladder

What it is

‘Production’ means measured, cited and trusted.

It means an evaluation harness with a real accuracy number, source citations on every answer, an explicit ‘I don't know’ when the documents don't support a confident answer, access-control awareness, and freshness and observability — the things that separate a trustworthy assistant from a chatbot that hallucinates.

What we deliver

Scope, stated up front.

Ingestion & retrieval

Connect your sources (SharePoint, Drive, Confluence, Notion, ticket exports, PDFs), clean and chunk them, and tune retrieval and re-ranking to your content.

An evaluation harness

A test set of real questions built with your experts, with a baseline and a final accuracy score — so improvement is demonstrated, not asserted.

Guardrails & citations

An explicit ‘I don't know’ behaviour and a source citation on every answer, so people can check it, not just believe it.

Deployment & operating model

In your environment, for a fixed outcome.

Deployed in your own environment — AWS, Azure or on-prem — with SSO, basic monitoring and audit. A UI or integration into Teams, Slack or your portal. Not a shared multi-tenant demo.

Your environment

AWS, Azure or on-prem — your account, your data, your access controls

Senior review

Senior technical review before delivery and before acceptance

Milestone payments

Fixed scope, fixed outcome, milestone payments — no lock-in

Next step

Start with a discovery call.

Thirty minutes with engineering — your use case, our experience, a scoped proposal.

Book a discovery call →Start with a Readiness Assessment

Common questions

Direct answers, before you commit.

How do we know the accuracy numbers are real?

The test set is written jointly with your team before we tune anything, and we report both the baseline and final scores — you see the starting point, not just the end result.

Will it hallucinate?

It is built to say ‘I don't know’ rather than guess when it isn't confident, and every answer includes a source citation so it can be checked before anyone relies on it.

Does our data leave our systems?

By default it is deployed inside your own environment — AWS, Azure or on-prem. Data handling is confirmed with you before any documents are ingested.

Our documents are messy — is that a problem?

It affects effort, not feasibility. We flag it during discovery so it is reflected in the scope up front rather than discovered mid-project.