SERVICE — RAG
An internal knowledge base that answers from your own documents
Retrieval-augmented generation (RAG) connects a language model to your files. Employees ask in plain language and get answers with source citations — not hallucinations.
INPDFs, wikis, manuals, tickets
→
OUTsourced answers in seconds
The problem
The knowledge exists — in handbooks, project folders, old tickets, the heads of two senior colleagues. Finding it costs every employee hours per week, and onboarding new people takes months because nothing is searchable in one place.
What we build
- Document ingestion — connectors for file shares, SharePoint, Confluence, email archives and ticket systems; automatic re-indexing when documents change.
- Retrieval that cites its sources — every answer links the exact passage it came from, so it stays verifiable.
- Access control — the assistant only answers from documents the asking employee is allowed to see.
- Your interface — a chat UI, a Slack/Teams bot, or an API into your intranet — whatever your team already uses.
- EU or on-premise deployment — vector database and model run on European infrastructure or your own hardware.
How it works
01
Corpus review
We look at your document landscape: formats, volume, quality, access rules. This decides chunking and retrieval strategy.
02
Prototype on real documents
Within 2–4 weeks you test a working assistant on a representative slice of your data.
03
Evaluation & hardening
We measure answer quality against a test set from your domain, fix failure modes and wire up permissions.
04
Rollout & operations
Integration into your intranet or chat tools, monitoring, and ongoing index updates.
Specifications
| Typical stack | pgvector or Qdrant, LangChain, EU-hosted LLM (e.g. Mistral, Azure OpenAI) or local model |
|---|---|
| Hosting | EU cloud or on-premise |
| Integrates with | SharePoint, Confluence, network drives, Slack, Teams |
| Prototype timeline | 2–4 weeks |
Frequently asked questions
Does our data end up training a public model?
No. Retrieval keeps your documents in your own vector database; the model only sees the passages relevant to a question, under a contract that excludes training use — or it runs entirely on your hardware.
What about hallucinations?
RAG grounds every answer in retrieved passages and cites them. When the corpus contains no answer, the assistant says so instead of inventing one — that behavior is part of our test set.
Which documents work?
PDF, Office, HTML, email, wiki exports and scans (via OCR). Messy, duplicated corpora are normal — cleaning them up is part of the project.
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