From Scattered Documentation to a Useful Internal Assistant
Every company faces the same problem: knowledge is fragmented. PDF manuals forgotten in a network folder, operating procedures on a SharePoint that nobody visits, emails with "the right way to do things," and the brain of that veteran employee who knows everything. When someone new arrives (or when the veteran is on vacation), productivity plummets.
The promise of corporate generative AI is to solve this through RAG (Retrieval-Augmented Generation) systems. But how do we go from theory to a bot that actually answers useful questions without hallucinating?
What is a RAG system really?
Imagine you hire a librarian with photographic memory. When you ask them a question, they don't invent the answer; first, they run to fetch the books (your documents) that talk about the subject, read the relevant paragraphs, and then build a summarized answer based only on what they have read. That is RAG. Unlike an open ChatGPT that pulls from its general training (and can invent), a RAG system is "anchored" to your corporate truth.
Step 1: Cleaning (Garbage In, Garbage Out)
The number one mistake is dumping everything into the AI. If you have three versions of the 2019, 2021, and 2024 procedure manual, the AI will get confused. Before touching a line of code or hiring a tool, you need to do digital archaeology:
- Delete duplicates and obsolete versions.
- Convert strange formats to clean text (scanned PDFs are the enemy).
- Structure the information: a document with clear headings and FAQs is much easier for the machine to understand than a 50-page wall of text.
Step 2: Chunking
AI models have a context limit. You can't pass the entire company history in every question. The secret of RAG is how you cut documents into small pieces (chunks). If you cut by paragraphs, maybe you lose context. If you cut by pages, maybe you include irrelevant information. The "chunking" strategy is the most artisanal and technical part of the process, and often where the battle for quality is won or lost.
Step 3: Citation, the key to trust
An internal assistant should never say "Do this." It should say: "According to the Occupational Risks manual (page 12), you must do this." The system must be able to show the original source. This has two advantages: it allows the human to verify the information (fundamental in legal or technical matters) and generates trust in the team, who stop seeing AI as a magic "black box."
Real Use Cases That Work Today
Where are we seeing the most immediate return?
- New Employee Onboarding: "How do I request vacation?", "What is the remote work policy?", "How do I configure the VPN?". The bot answers instantly, freeing up HR and IT.
- Level 1 Technical Support: Agents who can ask "What is error code 503 in the Amadeus API?" and receive the documented technical solution without having to escalate to a senior.
- Contract Analysis: "What penalty clauses does the contract with supplier X have?".
Conclusion: Start small, but start
Don't try to create the Oracle of Delphi on day one. Start with a single department (for example, Customer Service) and a single document corpus (FAQs and product manuals). Learn how your users ask questions, adjust retrieval, and gradually expand. Your company's knowledge is your most valuable asset; making it accessible is the best thing you can do for your efficiency.