An operational internal AI assistant in 10 business days
Deploying an AI assistant connected to the company's document base is no longer a 6-month project. Thanks to RAG (Retrieval-Augmented Generation) and no-code platforms, an SMB can put a functional assistant into production in 2 weeks for less than EUR 15,000. McKinsey estimates that internal AI assistants save 1.5 hours per day per employee on information retrieval tasks.
The problem: internal information cannot be found
In a mid-market company of 200 employees, staff spend on average nearly 2 hours a day looking for information: internal procedures, answers to HR questions, technical specifications, customer history. Several studies point to a productivity loss in the order of several thousand euros per employee per year.
Intranets are poorly indexed, wikis are out of date, documentation is scattered across SharePoint, Confluence, shared folders and mailboxes. New joiners take 3 to 6 months before they know where to find critical information.
An internal AI assistant centralizes this knowledge and makes it accessible in natural language. The employee asks their question, the assistant answers while citing the source document. No more navigating across 5 different tools.
The solution: a RAG assistant connected to your documents
RAG (Retrieval-Augmented Generation) combines a semantic search engine with a language model. When an employee asks a question, the system retrieves the relevant passages in your document base, then the LLM formulates a natural answer while citing its sources.
HR knowledge base
Answer instantly questions about leave, health insurance, remote work, expense reports. 60% reduction in requests to the HR department.
Internal technical support
The assistant guides technicians to the right maintenance or troubleshooting procedure. Resolution time reduced by 35% on level 1 incidents.
Sales support
Sales reps query the assistant about product sheets, pricing, discount terms. A 25-minute gain per preparation meeting.
Sprint roadmap: 2 weeks flat
Day 1-2: scoping and document selection
Identify the 3 priority use cases and select 200 to 500 source documents. Choose your RAG platform (Microsoft Copilot Studio, Dust, Botpress or the open-source LangChain + Qdrant stack). Define the access permissions.
Day 3-5: ingestion and indexing
Connect the document sources via the native connectors (SharePoint, Confluence, Google Drive). Launch the vector indexing. Test the chunking quality on 50 sample questions. Adjust the chunk size (500 to 1,000 tokens) and the overlap (10-15%).
Day 6-8: prompt engineering and testing
Configure the system prompt with the tone, the limits and the source citation instructions. Test on 100 questions covering the 3 use cases. Measure accuracy (target: 85%+) and fix hallucinations by adjusting the confidence threshold.
Day 9-10: deployment and pilot launch
Deploy the assistant on Teams, Slack or the intranet. Launch a pilot with 20 to 30 volunteer users. Collect feedback for 1 week, then gradually open it up to the whole company. Plan a feedback channel to keep improving.
Measured results
A management consulting firm in Lille with 85 employees deployed a RAG assistant on its internal procedures base (1,200 documents). In 6 weeks, the number of questions sent to the internal support service fell by 55%. Junior consultants estimate they save 45 minutes a day on document retrieval. The EUR 12,000 investment paid for itself in 10 weeks.
Frequently asked questions
How much does deploying an internal AI assistant cost?
For a functional MVP in 2 weeks, plan for between EUR 8,000 and 20,000 all in (platform, integration, support). The recurring monthly cost ranges from EUR 200 to 800 depending on the volume of requests and the chosen model.
Which documents can be connected to the assistant?
PDF, Word, Excel, PowerPoint, Confluence pages, Notion, SharePoint, archived emails. Standard connectors support more than 40 formats. Structured documents (procedures, FAQs, guides) give the best results.
Can the assistant hallucinate on internal information?
The risk exists but RAG (Retrieval-Augmented Generation) strongly reduces it by forcing the model to cite its sources. Add a confidence threshold: below 70%, the assistant replies that it does not know rather than making things up.
Recommended technical stack
RAG: LangChain + Qdrant + LLM
A Retrieval-Augmented Generation pipeline combining a vector search engine, an orchestration framework and a language model. Cloud or on-premise compatible.
Pricing
Comparison
| Criterion | Open-source stack | Copilot Studio | Dust.tt |
|---|---|---|---|
| Customization | Full | Limited | Moderate |
| Ease of deployment | Complex | Simple | Simple |
| Data sovereignty | Full | Microsoft | EU |
| Cost for 50 users | 300 EUR/month | 400 EUR/month | 1,450 EUR/month |