Enterprise RAG: making AI answer with your sources
Give AI access to your internal documents for sourced, traceable and reliable answers — without hallucinations.
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RAG (Retrieval-Augmented Generation) connects an LLM to your internal documents to provide sourced and traceable answers. Your teams query the document base in natural language and get answers with citations.
In 30 seconds
RAG (Retrieval-Augmented Generation) allows an AI to answer based on your internal documents: the relevant excerpts are retrieved, the answer is generated, and citations and traceability cues are kept to limit hallucinations. It's the most pragmatic route when AI has to answer about your rules, your contracts or your procedures.
Typical problems
The signs that this solution is right for you.
Scattered and inaccessible documents
Your procedures, contracts and knowledge bases are in 3 different systems. Nobody finds the information in time.
False answers from LLMs
ChatGPT doesn't know your internal rules. If you ask it a business question, it makes things up. RAG forces the AI to cite its sources.
No traceability
When a colleague gives an answer, you don't know where it comes from. A RAG system systematically cites the source document.
Our approach
A proven method, in clear steps.
Ingestion and cleaning
We connect your document sources, clean up the formats, deduplicate and manage versions. Each document is segmented (chunking) with its metadata.
Semantic indexing
The segments are converted into vectors and indexed in a vector database. The index enables search by meaning, not by keywords.
Retrieval + generation pipeline
When a user asks a question, we retrieve the most relevant segments, inject them into the prompt, and the LLM generates a sourced answer.
Quality evaluation
We measure accuracy (faithfulness), coverage (recall), latency and the hallucination rate. Goal: 90%+ faithfulness to the sources.
Security and access rights
We implement role-based access control: each user only sees the documents they are entitled to. Full logging.
What you get
- Operational RAG pipeline (ingestion → retrieval → generation)
- Vector database indexed with your documents
- Conversational search interface
- Systematic citations with a link to the source document
- Quality dashboard (faithfulness, coverage, latency)
- Technical documentation and user guide
When to use RAG (and when not to)
RAG is relevant when: (1) you have internal documents the AI must consult, (2) these documents change regularly, and (3) the traceability of answers is important. It is not suited for creative tasks (marketing content generation) or cases where the model has to learn a specific behavior (fine-tuning).
Reference architecture
A robust RAG comprises 4 layers: (1) ingestion (connectors, cleaning, chunking), (2) indexing (vector database + metadata), (3) retrieval (semantic retrieval + reranking), (4) generation (prompt engineering + citations). Each layer has its own quality metrics.
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