Enterprise RAG: query your internal documents with confidence

In many companies, the information exists but nobody can find it: procedures on an intranet, contracts in a document management system, technical documentation in a wiki, standard replies buried in mailboxes. An enterprise AI assistant connected to these documents lets people ask a question in natural language and get a sourced answer within seconds.

The underlying technique is called RAG, for retrieval-augmented generation: the system first retrieves the relevant passages from your documents, then the model writes an answer based on those passages alone. For a full explanation of the principle, its variants and its limits, see our article RAG for business: definition, how it works and use cases. This page describes how we build these assistants so that they hold up in production.

The most common use cases

• Internal support and helpdesk: answering employees’ questions about HR, IT or quality procedures.
• Sales and customer service teams: finding a clause, a product feature or an approved answer.
• Technical teams: querying operations documentation, release notes and runbooks.
• Legal and compliance: navigating a body of contracts or internal policies, with systematic citation of sources.

In every case, the assistant helps people find and phrase information; the decision remains human.

What makes a document assistant good

The perceived quality of an internal chatbot depends less on the chosen model than on the pipeline that feeds it. We work on every link in that chain.

Source ingestion

We identify the reference sources and their owners, then set up connectors that retrieve documents and their updates. Office formats, PDFs, internal web pages, tickets: each format has its traps (tables, repeated headers, scanned documents). Outdated or duplicate documents are a document governance problem that the assistant makes visible: it is better to fix it than to index it.

Chunking and enrichment

Documents are split into passages of a suitable size, respecting their structure (headings, sections, articles). Each passage keeps its metadata: source, date, version, author, access rights. This metadata is used for filtering, citation and keeping answers up to date.

Search

Search often combines semantic search (by meaning) and keyword search (for references, product codes, proper names), with re-ranking of the results. The choice of index depends on your volumes, your cloud and your operational skills: a managed service such as Amazon Bedrock Knowledge Bases, an existing search engine, or a database with a vector extension.

Access rights

This is the most frequently underestimated point. An assistant connected to all of the company’s documentation must not become a way to bypass access rights. The permissions of each document are carried over at indexing time and applied to every query based on the user’s identity, taken from your directory.

Generation and citations

The model receives the question and the authorised passages, with strict instructions: answer from the sources, cite them, flag when information is missing. An answer without a verifiable source is a warning sign, not an answer.

Evaluating answer quality

Without measurement, there is no way to know whether a change improves or degrades the assistant. Together with your subject-matter experts, we build a set of representative questions, with the expected answers and documents. We then measure separately:

What we measure	Question asked	What it reveals
Retrieval relevance	Are the right passages retrieved?	Ingestion, chunking or indexing problems
Answer faithfulness	Is the answer based only on the sources?	Risk of hallucination
Correctness	Is the answer right according to the expert?	Overall perceived quality
Justified refusals	Can the assistant say it does not know?	Robustness to out-of-scope questions

These evaluations are rerun after every change of model, prompt or configuration, in line with our LLMOps production deployment approach. User feedback (helpful answer or not) completes the set-up.

Confidentiality and hosting

Your documents are often the most sensitive thing a company has. We design the architecture so that you stay in control:

• hosting in your AWS or Azure account, in the region of your choice;
• access to models through the cloud provider’s managed services, with data processing terms checked and documented;
• encryption at rest and in transit, network isolation, dedicated identities;
• logging of questions and answers, with a retention period defined with regard to the GDPR;
• entry of the use case in the company’s AI register, linked to your AI governance and EU AI Act compliance.

Our approach and deliverables

1. Scope: document scope, users, typical questions, confidentiality requirements, quality criteria.
2. Build: ingestion pipeline, indexing, search, interface or integration into your tools (intranet, collaborative messaging), first evaluation dataset.
3. Industrialise: deployment as Infrastructure as Code, automatic source synchronisation, monitoring, cost tracking per use case.
4. Improve: analysis of unanswered questions, enrichment of the corpus, new sources.

Deliverables: documented architecture, assistant deployed in your environment, evaluation dataset and quality report, usage and cost dashboard, operations documentation and knowledge transfer to your teams.

Pitfalls to avoid

• Indexing everything at once: start with a controlled corpus and a defined audience, then widen.
• Neglecting freshness: an assistant that cites an outdated procedure loses users’ trust within days.
• Forgetting access rights: address them in the architecture, not after the demo.
• Judging on three questions: only an evaluation on a representative set tells you whether the assistant is ready.

We work with companies in the Lille metropolitan area (northern France) and beyond that want a first assistant on a targeted scope, or that have a prototype to make reliable before opening it to all teams.

[ YOUR QUESTIONS ]

Frequently asked questions

How do you implement RAG in a business?

You start by choosing a document scope and typical questions, then set up document ingestion, chunking, indexing and search. The model then generates an answer from the passages retrieved. The most important part is often evaluation: measuring on real questions whether the answers are correct and sourced.

Can an internal chatbot respect document access rights?

Yes, provided it is designed that way from the start. The permissions of each document are kept during indexing and filtering is applied at search time, based on the user’s identity. An assistant must never retrieve a passage that the user could not open themselves.

Are our documents sent to OpenAI or another vendor?

It depends on the architecture chosen. By using model services hosted in your own cloud account, such as Amazon Bedrock or Azure OpenAI, in a region you choose, you keep control over hosting and access. The data processing and retention terms of each service must be checked and documented.

How do you prevent an AI assistant from hallucinating?

You cannot eliminate hallucinations entirely, but you can reduce them and make them detectable. The assistant answers only from the passages retrieved, cites its sources and is able to say it cannot find the answer. An evaluation dataset tracks the faithfulness of answers over time.

Do we need to train a model on our documents?

In most cases, no. RAG lets you use your up-to-date documents without retraining the model, and remove a document simply by deleting it from the index. Fine-tuning a model is more justified for a very specific answer style or format.