Your employees spend 1.8 hours per day searching for information — a RAG chatbot reduces this time by 60%
Several studies suggest that employees devote on average nearly 2 hours per day to searching for information in the company's internal documents. For a mid-market company of 200 people, this represents 90,000 hours per year of lost productivity. The RAG (Retrieval-Augmented Generation) technology makes it possible to deploy a chatbot that queries your document base directly and provides sourced answers in natural language.
The problem
Document management in SMBs and mid-market companies suffers from three structural ailments that classic tools do not solve:
Dispersed and siloed information
Documents live in SharePoint, Google Drive, file servers, mailboxes and sometimes individual hard drives. The same process can be documented in three different places, with three contradictory versions. New arrivals take on average 3 months before they know where to find the relevant information.
Inefficient keyword search
The native search of SharePoint or Google Drive relies on exact keywords. If you search for "customer refund procedure" but the document is titled "After-sales return policy", you will not find it. Employees end up asking a colleague who "knows where it is" — a fragile and non-scalable system.
Captive and undocumented expertise
20% of the company's critical knowledge is documented nowhere: it resides in the heads of experienced employees. When these people go on holiday, fall ill or leave the company, the information disappears. The RAG chatbot does not solve this problem directly, but it encourages documentation to feed the knowledge base.
The AI solution
A RAG chatbot combines the power of an LLM with your own document base. Here is how it works and what it brings at each step:
Ingestion and intelligent chunking
Your documents are automatically extracted, split into fragments (chunks) of 500 to 1,000 tokens and transformed into numerical vectors. This process preserves the context of each fragment: document title, chapter, modification date. Indexing 10,000 documents takes about 2 hours.
Semantic search and reranking
When a user asks a question, the system searches for the most relevant fragments by semantic similarity — not by keywords. A reranking model then refines the ranking. The result: the answer relies on the 5 to 10 most relevant passages from your entire base, whatever their location or vocabulary.
Sourced answer generation
The LLM synthesizes the retrieved fragments to formulate a clear and structured answer in natural language. Each statement is accompanied by a reference to the source document (name, page, date). The user can click on the source to verify. This citation mechanism reduces hallucinations by 90% compared to an LLM used alone.
Implementation
Deploying a RAG chatbot follows a four-step path, from prototype to production:
Weeks 1-2: Document audit and scoping
Identify the priority document sources (those that generate the most recurring questions). Estimate the volume: number of documents, formats, total size. Define the scope of the POC: one department, one document type or one business process. Involve end users to collect the 50 most frequent questions — they will serve as a test set.
Weeks 3-5: Ingestion pipeline and vector database
Set up the extraction pipeline: SharePoint/Drive connectors, PDF/Word parsers, chunking with 10% overlap. Deploy a vector database (Qdrant recommended for its simplicity). Generate the embeddings with a model such as text-embedding-3-large. Test the search quality on your 50 reference questions and adjust the chunk size and chunking strategy.
Weeks 6-8: Conversational interface and integration
Develop the user interface: a web widget embeddable in your intranet or a Teams/Slack application. Configure the system prompt to guide the LLM's answers (tone, citation format, scope). Integrate SSO authentication to control access by department. Add a feedback mechanism (thumbs up/down) to improve quality over time.
Weeks 9-12: User testing, iteration and go-live
Deploy the chatbot to 20 to 30 pilot users. Collect feedback for 3 weeks: unanswered questions, incorrect answers, missing sources. Adjust the pipeline (adding documents, modifying chunking, refining the prompt). Measure the resolution rate (target: 75% at launch). Gradually open up to the entire company with structured internal communication.
Expected results
Frequently asked questions
Can the RAG chatbot invent false answers?
The risk of hallucination exists but it is considerably reduced by the RAG mechanism. The model only answers from the indexed documents and cites its sources. By adding a confidence threshold (minimum similarity score), you can make the chatbot answer "I don't know" rather than inventing.
Which document formats are supported?
Common formats are natively supported: PDF, Word, PowerPoint, Excel, HTML, Markdown and plain text. For specific formats (CAD, business software), a custom extraction connector is required, which adds 1 to 2 weeks to the project.
Does the data stay within our infrastructure?
Yes, if you wish. The architecture can be entirely on-premise with a self-hosted LLM (Llama 4 or Mistral). If you opt for a cloud API (Claude, GPT), the data passes through the provider but is not stored if you use the enterprise options with a contractual commitment.
How many documents can be indexed?
There is no theoretical limit. In practice, a vector database such as Qdrant easily handles 10 million chunks (fragments) on a modest server. For an SMB with 50,000 documents, this represents about 500,000 chunks, or 5% of the capacity. The storage cost is negligible.
For technical profiles
Recommended RAG architecture
Generation LLM
Excellent price/quality ratio for generating long, sourced answers. 200k token window allowing 15 to 20 context chunks to be injected without compression. Average cost of 0.01 € per question with a standard RAG context of 4,000 tokens.
Vector database + embeddings
Qdrant is open source, self-hostable and performant up to 100 million vectors. Combined with OpenAI's 3,072-dimension embeddings, semantic search reaches 92% recall@10 on the MTEB benchmarks. Sovereign alternative: Mistral embeddings model (1,024 dimensions, hosted in France).
Estimated monthly pricing
Quick comparison
| Criterion | Custom RAG | Microsoft Copilot | Glean |
|---|---|---|---|
| Customization | Total | Limited | Medium |
| Monthly cost (200 users) | ~455 € | ~6 000 € | ~4 000 € |
| Sovereign hosting | Possible | No | No |
| Deployment time | 8-12 wks | 1-2 wks | 2-4 wks |