RAG: AI That Answers With Your Documents
Allow an AI to first search the company's documents, then answer from that information — with sources.
Hundreds of documents, unfindable answers
A company accumulates hundreds of PDFs, procedures, contracts and internal documents. The information exists — somewhere — but:
- finding it requires knowing where to look;
- classic search engines only find exact words;
- documents are scattered across folders, messaging and software;
- a precise question ("what to do in case of supplier dispute?") stays without a quick answer.
Result: hours lost, inconsistent answers depending on the person, and expertise sleeping in files.
What is RAG, simply
RAG stands for "Retrieval-Augmented Generation": generation augmented by retrieval.
The principle: before answering, the AI first searches the company's documents. It identifies relevant passages, then answers from those passages — and can cite its sources.
Why it matters:
- an AI alone answers with general, sometimes wrong knowledge;
- an AI with RAG answers with your documents, your procedures, your rules;
- answers are verifiable: every claim points to a passage;
- company knowledge is updated simply by adding documents.
Semantic search is the technology that makes this possible: it finds passages whose wording differs but whose meaning is close to the question.
Document assistants plugged into your information
Technea builds complete RAG systems:
- semantic search: finding the right passage without knowing the exact words;
- document assistants: asking a question, getting a sourced answer;
- company knowledge bases: internal expertise finally consultable;
- integration with existing tools: the assistant in the intranet or business application.
The RAG studies carried out in the Lab (chunking strategies, embedding models, retrieval strategies) guarantee measured and documented technical choices.
Concrete examples
- Document search: finding information in thousands of pages.
- Knowledge base: an internal assistant answering from procedures.
- Document analysis: understanding and summarising a large file.
- Newcomer onboarding: common questions get an immediate answer.
How a RAG system is built
1. Document inventory: formats, volumes, sensitivity. 2. Content preparation: passage splitting, cleaning. 3. Indexing: passages transformed for semantic search. 4. Answer tuning: AI answers with passages and sources. 5. Evaluation: tests on real questions, adjustments.
What it changes
- answers in seconds instead of hours of searching;
- answers grounded in your documents, not assumptions;
- cited sources: every answer is verifiable;
- knowledge that stays up to date: adding documents is enough;
- shared expertise: everyone accesses the same knowledge.
Frequently asked questions
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Your documents contain the answers?
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