RAG & Knowledge Bases
AI that answers from your documents — with sources
At a glance: retrieval-augmented generation (RAG) connects an LLM to your documents, wikis and databases so it answers from your actual content — with citations — instead of from generic training data. It is the architecture behind grounded chatbots, internal knowledge assistants and compliant document workflows.
What we deliver
- Retrieval architecture — chunking strategy, embeddings, vector or hybrid search, and re-ranking tuned on your corpus, not on a demo dataset.
- Citation & traceability — every answer carries its source document and passage, so users can verify and auditors can trace.
- Evaluation sets — question/answer benchmarks built from your real questions, run before every change.
- Connectors — to your document stores, wikis, ticketing and databases, with fresh-index scheduling.
- On-prem option — the whole RAG stack can run on your infrastructure with open-weight models.
Why RAG beats fine-tuning for knowledge
Always current
Update a document and the answers update — no retraining cycle, no stale model.
Traceable
Answers cite their sources. In regulated industries this is the difference between a demo and a deployable system.
Access-aware
Retrieval respects your permissions: users get answers only from documents they are allowed to see.
Cheaper than training
No model training to maintain — the intelligence lives in your retrieval setup, which you can evolve continuously.
How we work
Discover
We map the knowledge domains, document formats and access rules.
Design
We design chunking, retrieval and reranking, then build the evaluation set from your real questions.
Deploy
We ship the RAG pipeline with citations, connectors and monitoring.
Enable
We train your team to extend the knowledge base and read the quality dashboards.
Common use cases
Internal knowledge assistant
One assistant over wikis, procedures and past tickets for the whole company.
Customer support grounding
Support agents — human and AI — answer from your docs with citations.
Contract & policy Q&A
Ask questions across your contract base and get sourced answers.
Technical documentation
Instant, sourced answers for engineering and field teams.
Pricing
Scoped RAG builds typically run €1,500–€10,000 depending on corpus size and connectors — confirmed by a quote.
Pair this with our conversational AI service for a customer-facing assistant, or with open-source AI deployment to keep everything on-premise.
Frequently Asked Questions
What is RAG, in plain terms?
Instead of asking the model to remember your content, RAG lets it look it up: the system searches your documents for relevant passages and the model answers using those passages, citing them. It is the standard architecture for enterprise assistants that must be accurate and traceable.
RAG or fine-tuning?
For knowledge, RAG — it stays current and traceable. Fine-tuning changes behavior or style, not facts. Most enterprise projects need RAG first; fine-tuning is rarely the right tool for knowledge grounding. See our model fine-tuning service when behavior is the problem.
How do you prevent wrong answers?
Three layers: retrieval restricted to your approved corpus, citations so every claim is checkable, and evaluation sets from your real questions run before every change. When the answer is not in the corpus, the assistant says so.
Can it respect document permissions?
Yes — retrieval filters by the querying user's permissions, so users only get answers from documents they are allowed to see. This is a hard requirement we design for from the start, not an afterthought.