Custom document search and knowledge systems for regulated organizations

Ask your own procedures, contracts, and scanned archives a question in natural language, and get back an answer that cites the paragraph it came from.

Scope a PoC on your own documents
ai knowledge base hero

9,000 documents. No shared way to search them.

One institution we scoped counted 9 000 documents across SharePoint, network shares and three legal databases. No shared catalogue. A third of it scanned. If that looks like your own map, you already know what happens next:

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Knowledge is scattered across a dozen systems

Procedures on SharePoint, contracts in Confluence, a network share nobody fully indexed, and at least one system only two people still know how to query. Nothing you own searches across all of it at once.
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Keyword search can’t answer your real questions

It has no idea two of your words mean the same thing, or that a document was superseded last June.
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Your archive is invisible

Scans and photocopies sit outside search entirely, so the older the answer, the harder it is to reach.
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Your expertise is a single point of failure

People ask the colleague who knows. That works until she retires, and 15 years of context leaves with her.
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Public AI tools get ruled out early

GDPR, professional secrecy, DORA, confidentiality clauses: whichever applies to you, it usually ends the discussion.

Three parts, all of them running inside your infrastructure.

From your file shares to a cited answer

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Read-only, against an account you control

Connectors run against a technical account you issue. Bad scans get deskewed, binarized and put through OCR with the right language pack before anything reaches the index.
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Semantic search over context-preserving passages

Documents get split into passages that keep their surrounding context, then embedded and indexed. A question about “termination clauses” finds the paragraph that says “early exit provisions,” even when the words never match. Chunk size, embedding model and index structure get tuned to what you actually send us. A 40-page contract and a one-line policy update aren’t chunked the same way.
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Q&A, coverage checks, batch runs

Conversational Q&A for daily work. A coverage check that flags which topics the knowledge base can’t answer yet, so you know where documentation is missing or out of date before someone finds out the hard way. Batch mode when one question needs answering across thousands of documents at once and the answer needs to land in a spreadsheet.

One place to ask, with the source attached to every answer

We build a knowledge layer over the sources you already have, in whatever state they are in.

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Every answer arrives instantly, with its source attached

File, page and paragraph, right where the answer appears. Checking it takes one click, quick enough to do while you’re still on the phone with the person who asked.
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Nobody’s exit costs you the context

The legal and operational judgment calls that used to live in one person’s head get captured once and stay searchable after she’s gone.
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Nothing leaves your walls

The system runs on hardware you control. In environments with no internet access at all, it still works.
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Conflicts get flagged automatically

When two documents disagree, the system says so instead of picking one at random. The expensive mistakes come from acting on a procedure that stopped being current two amendments ago.
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Your permissions decide what gets answered

Roles map from your AD groups. Ask about something outside your access, and the system stays quiet rather than guessing.
From workshop to production, with a decision gate
Discovery [2-3 weeks]

Workshops with IT, compliance and the business. We map candidate use cases, score the business case and agree the metrics we will be judged on.

Deliverable: use case map, target metrics, architecture recommendation.

PoC on your documents [6-8 weeks]

Built on your documents, tested by a real user group, measured against the metrics from Discovery. The range depends on volume and the state of your sources.

Deliverable: a KPI report with precision, recall and F1 on your own corpus.

Pilot / MVP [6-10 weeks]

One department or one collection, live for real users, with the integrations that scope needs.

Deliverable: a working system on a limited scope.

Production rollout [4–6 weeks]

Hardening, high availability, access control from AD, audit trail, integration with your SIEM, DLP and document repositories. UAT, training, go-live.

Deliverable: a system handed over to your team to run.

Managed ongoing (optional)

Reindexing, model updates, retrieval quality monitoring, patching. Your IT team can run it all. Plenty of clients keep us on the retrieval side, because that is the part that needs judgment.

Testimonial

What clients tell us, on the record

By automating certain customer interactions, bank employees are provided with a prepared “semi-product”, which enables them to dedicate more time to personalizing and empathizing with customer communication, and thus taking even better care of their needs.

Katarzyna Tomczyk – Czykier
Director of the Innovation and Digitization Division – Retail Banking
Bring your document sources and your constraints
45 minutes with a solutions architect who has written the RFI answers for institutions like yours. Come with your source systems and your worst-case document. If the answer is that Copilot solves your problem for less money, we’ll say that too.
Book scoping call

let’s get in touch

Contact our consultant

Book 1-on-1 consultation 

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Grzegorz Motriuk

Head of Sales | Application Development

Our consultant is at your disposal from 9 AM to 5 PM CET working days from Monday to Friday for any additional questions.

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Questions from the first meeting

  • How is this different from Microsoft Copilot?

    Copilot processes your documents in Microsoft’s cloud and assumes your M365 permissions are already accurate. If both are fine for you, Copilot is probably cheaper than us. Call us if either isn’t.

  • What happens when the model doesn’t know the answer?

    It reports low confidence instead of inventing a paragraph. Answers are grounded in retrieved passages and every claim carries its source, so a reviewer checks it in one click.

  • Do we need our own GPUs?

    For a fully local deployment, yes. A recent public-sector spec came out at 2 servers with 4 cards each for 1 000 concurrent users. Yours could be a single server, and we’d rather load-test than guess.

  • Can it read scanned documents?

    Collection-level access control mapped from AD groups, applied before ranking. Restricted material never reaches the model’s context for an unauthorized user.

  • How do you keep confidential collections separate?

    Collection-level access control mapped from AD groups, applied before ranking. Restricted material never reaches the model’s context for an unauthorized user.

  • Can we start with one department?

    Usually the right way in. One collection, one user group, success measurable in weeks. Adding sources later is configuration, not a rebuild.

  • Who owns the code?

    You own the integration and application layer under MIT. The components underneath are open source. There’s no key we can turn off.

  • What does maintenance involve?

    Daily incremental reindexing, model version updates, retrieval quality monitoring, and patching in your air-gapped registry. Your IT team can run all of it after handover.