RAG & Enterprise Knowledge
We make internal knowledge from documents, tickets, wikis, mails, and filesystems findable, usable, and traceable through AI. Instead of a plain language model that answers "how it must sound", we deliver RAG systems that answer "how it is actually documented at your company" — with source links, access model, and audit trail.
For whom, what problem, what outcome
Companies with grown documentation, whose knowledge is scattered across Confluence, SharePoint, Nextcloud, filesystem, inboxes, and ticket histories. Where new colleagues spend months getting lost in searches and old hands are the only ones who know where "that one test report from 2018" sits. Result: faster knowledge access, shorter onboarding, less dependence on individuals, better decisions with traceable sources.
Typical use cases
- RAG systems across Confluence, SharePoint, Nextcloud, filesystem as a unified knowledge layer
- Smart document search with source links — the agent cites where the answer came from
- Knowledge assistants for service desk, onboarding, HR, engineering
- Access concept — the agent shows only what the asking person is allowed to see
- Source link and version awareness — no answer from outdated docs without warning
- Indexing heterogeneous sources (PDF, DOCX, Markdown, HTML, email threads, ticket histories)
- Data preparation — OCR for scanned documents, table extraction, metadata enrichment
- Integration into chat front-ends, portals, service desk tools, internal applications
How we work
- Map the knowledge landscape — what is where, at what quality, with what freshness? Which sources are permanent, which temporary? Who is allowed to see what?
- Pick the pilot use case — we typically start with service desk or onboarding, because impact is measurable quickly and data is plentiful.
- Indexing & vector store — chunking strategies, embeddings (local or API), vector store (pgvector, Qdrant, Weaviate) incl. permission handling.
- RAG architecture — query reformulation, hybrid search (BM25 + vector), re-ranking, answer synthesis with mandatory citations, hallucination guards.
- Operations & quality — eval set from typical queries, continuous evaluation, update pipeline for new docs, drift alert on degraded recall.
Tech stack
Deliverables
- RAG system with source links, citation requirement, hallucination guards
- Indexing pipeline with update paths for new/changed documents
- Access model respecting ACLs from Confluence/SharePoint/filesystem
- Eval suite (recall, precision, answer faithfulness) with regression tests
- Front-end integration (web chat, MS Teams, Slack, embed in internal app)
- Operations runbook incl. document care and drift response
Customer benefit
- Faster knowledge access — answers in seconds, not hours of search
- Better onboarding — assistant is always available
- Less dependence on individuals who "know everything"
- Better decisions through cited sources instead of gut feeling
- Usable internal knowledge — what would otherwise dust folders becomes queried again
Compliance & security
- ACL respect: the agent shows only what the asking person could see anyway
- Local embeddings and models for sensitive data — no transfer to third parties
- Version awareness: answers from outdated docs are flagged or suppressed
- Audit log for all queries — who asked what when, which sources were delivered
- GDPR-compliant storage of query logs with retention periods
FAQ
Difference from full-text search?
Full-text finds exact terms. RAG finds semantic hits — even when the asker writes "supplier invoice with discount logic" and the document says "incoming invoice with discount field". Hybrid search combines both so neither synonyms nor exact hits are lost.
What if our docs are bad or outdated?
Then we see it. RAG ruthlessly exposes doc gaps and contradictions. Many customers use the introduction phase to clean docs — the agent supports with conflict reports and "frequently asked, poorly answered" lists.
Does everything stay local?
Optional yes. Local embeddings (BGE, E5) and local language models (Gemma, Llama, Mistral) on your infrastructure — no outbound data flow. Cloud APIs only where DPA and locality allow and the value is wanted.
How do you prevent hallucinations?
Four layers: (1) RAG requirement: every answer needs at least one source. (2) Citation validation: the agent must show where the statement is. (3) Confidence thresholds with answer refusal. (4) Eval suite with answer-faithfulness score as regression gate.
How long does introduction take?
8–12 weeks to the first productive RAG use case (service desk or onboarding). Most time goes into clean indexing, permission model, evaluation — early hallucinations would cost trust, so we take care.
Discuss your knowledge use case
Which question is asked multiple times daily and should really be answered already? We assess whether a RAG system delivers impact short-term.
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