[ SOLUTIONS ] / [ ENTERPRISE RAG KNOWLEDGE BASE ]

Company documents, searchable like ChatGPT.

Employees lose hours hunting for the right procedure, the latest template contract, the tax example a colleague worked on. We build a conversational AI on top of your knowledge base that answers by citing sources, with role-based access governance.

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[ THE PROBLEM ]

What happens today.

Company knowledge is everywhere and nowhere: SharePoint, shared Drives, network folders, email, Notion, Confluence, contracts on file servers. Finding the latest version of a document is an archaeology exercise. Questions like 'how did we handle a similar case last year?' take half an hour to answer.

A conversational AI on RAG (retrieval-augmented generation) answers questions citing source documents (with links), respects access permissions (a junior doesn't see confidential contracts), and keeps an audit log of every query. No more 'the AI hallucinated': the answer is anchored to real documents.

Writing the knowledge down isn’t enough. It has to be indexed so it can be recalled in 3 seconds.

[ HOW IT WORKS ]

The solution, broken into parts.

  • Multi-source indexing

    Ingest from SharePoint, Google Drive, Notion, Confluence, file servers, network folders. Documents are indexed with embeddings and metadata. Automatic incremental updates (new documents are indexed without a full rebuild).

  • Role-based access

    Permissions follow those of the source file system: a marketing employee does not see confidential HR documents, even if the AI knows them. RBAC or ABAC, integration with Active Directory or Google Workspace.

  • Answers with citations

    Every answer references the source document with a direct link. No hallucinations: if the AI does not have material to answer, it says so explicitly instead of inventing. Structured audit log of every query and every cited document.

[ WHO IT'S FOR ]

The typical profiles who benefit.

  • Professional firms with accumulated knowledge

    Law firms, accountants, labour consultants: years of opinions, template contracts, precedents. A new hire needs months to know 'where a given thing is written down.' AI drastically shortens onboarding.

  • Manufacturing companies with complex procedures

    Operating manuals, product sheets, quality procedures, certifications. AI answers operational questions ('how do I fill out form X', 'what is the torque value for bolt Y') in real time, on the shop floor.

[ WHAT WE NEED ]

Transparency on what the client does.

Before we start we need a few accesses and decisions. All reasonable, no surprise asks.

  • Document source access

    • Read access to SharePoint / Drive / Notion / Confluence / file servers
    • ACL mapping: who sees what, what company roles exist

  • Operational decisions

    • List of sources to include in the first round (start small, expand later)
    • Confidentiality policy: documents that must NEVER enter the RAG
[ TIME AND COST ]

Indicative numbers, not quotes.

TIME
Typically 6-12 weeks for the base version. Very large knowledge bases (>100k documents) need preliminary cleanup phases.
COST
Range €18,000-50,000 for the initial version. Operating LLM cost €100-800/month depending on volume and knowledge-base size.
MODEL
Fixed milestones. Soft launch on a pilot group, progressive scaling to other teams.

Indicative numbers. For an accurate quote, let's talk.

[ FREQUENTLY ASKED ]

Answers to the most common questions.

Do confidential documents stay confidential?

Yes. The source file system read permissions are respected: the AI may know a document exists, but if the asking user does not have permission on it, the AI does not cite it nor use it to answer. Standard integration with Active Directory, Google Workspace, SharePoint ACLs.

Can the AI make information up?

The RAG pattern anchors answers to source documents. If the AI has no material, it is instructed to say so explicitly instead of inventing. All answers also include source citations so the user can verify. The 'hallucinations' typical of bare ChatGPT are drastically reduced.

How often is the knowledge base updated?

Updates are incremental: new documents added to the sources are indexed within 5-30 minutes. Edited documents are re-indexed automatically. No periodic rebuild needed, the AI is always aligned with documentary reality.

[ LET'S TALK ]

Recognize your case?

Write a couple of lines about your context. We'll reply within 24-48 hours with an initial assessment and a first orientation on time and cost.

Let's talk
[ RELATED SOLUTIONS ]
[ WHERE IT WORKS BEST ]
[ PARENT PILLAR ]
AI agents and LLM integration We don't replace your systems. We give you AI agents that integrate with them.