The verdict in three sentences
A RAG assistant (retrieval-augmented generation) indexes your internal documents and answers by citing its sources, without inventing procedures. In 2026, budget EUR 25,000 to 60,000 (about USD 27,000 to 65,000) to build it and EUR 500 to 2,000 per month to run it for 200,000 documents hosted in the EU or the US. The real challenge is not the language model but access-rights management: an assistant that shows a confidential contract to the wrong department is a failure, however fast it answers.
What you are actually buying: cost items
A serious RAG project has four building blocks: document ingestion (SharePoint, file servers, DMS, scanned PDFs), chunking and vectorization, a hybrid search engine (semantic plus keyword), and a chat interface with citations. Generative AI itself often accounts for less than 20% of the budget.
| Item | 2026 range (excl. VAT) | Comment |
|---|---|---|
| Scoping, source and permissions audit | EUR 3,000 to 6,000 | 2 to 3 weeks, repository mapping |
| Ingestion connectors (SharePoint, DMS, network drives) | EUR 6,000 to 15,000 | Higher with OCR on drawings and scans |
| Chunking, vectorization, hybrid index | EUR 5,000 to 12,000 | 200,000 documents, incremental updates |
| Access-rights filtering (ACL, Active Directory) | EUR 4,000 to 10,000 | Essential, often underestimated |
| Chat interface with citations and user feedback | EUR 4,000 to 10,000 | Web, Teams or intranet |
| Quality evaluation, test set, tuning | EUR 3,000 to 7,000 | 200 to 400 reference questions |
| Total build | EUR 25,000 to 60,000 | Typical timeline: 3 months |
The low end assumes clean documents in a single repository. The high end covers several sources, many scans and fine-grained rights per project or client.
Monthly cost: inference, hosting and maintenance
Recurring cost depends on question volume and hosting choice. For sensitive engineering data, we recommend regional hosting (EU region for European firms, US region for North American ones) with models whose data is not used for training.
| Monthly item | 100 active users | 400 active users |
|---|---|---|
| LLM inference (API, regional) | EUR 200 to 400 | EUR 600 to 1,100 |
| Vector database and storage | EUR 80 to 150 | EUR 150 to 300 |
| Incremental ingestion compute | EUR 50 to 100 | EUR 100 to 200 |
| Monitoring, logs, backups | EUR 50 to 100 | EUR 80 to 150 |
| Application maintenance (flat fee) | EUR 120 to 250 | EUR 200 to 300 |
| Estimated total | EUR 500 to 1,000 | EUR 1,130 to 2,050 |
These amounts are a 2026 order of magnitude, based on 15 to 25 questions per user per week. Running a model on your own GPUs only pays off beyond several thousand users.
Access rights, compliance and answer quality
Three rules prevent most failures. First, rights filtering happens before retrieval: the assistant only sees documents the user could already open. Second, every answer cites the document, page and version date so the engineer can check in one click. Third, a dashboard tracks the useful-answer rate (target: above 80% after tuning) and unanswered questions, which often reveal missing documents.
On compliance, the knowledge base holds personal data (CVs, minutes, emails). Under GDPR in Europe, a processing record, a light impact assessment and a retention period for chat logs (90 days is common practice) cover most cases. In North America, align with your SOC 2 controls and state privacy laws.
Mini case study
Thomas, CIO of a 400-engineer design and engineering firm in Lyon, finds that each engineer spends 5 hours a week looking for calculation notes, internal standards and lessons learned. The average loaded cost of an engineer is EUR 55 per hour.
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- Current time lost: 400 × 5 h × 45 weeks = 90,000 hours a year, or EUR 4,950,000.
- With search time divided by three: 30,000 hours, so 60,000 hours saved.
- Even if only 20% of that gain is truly redeployed to billable work: 12,000 h × EUR 55 = EUR 660,000 a year.
- First-year cost: EUR 55,000 build + 12 × EUR 1,800 = EUR 76,600.
Payback is counted in weeks, even under a very cautious assumption. Thomas starts with a 6-week pilot in two departments (80 engineers) to measure the real gain.
FAQ
Can a RAG assistant make up answers?
The risk exists but drops sharply when the model must answer only from retrieved passages and cite its sources. With a 300-question test set, the target is under 5% unsourced answers before go-live.
Do we need to clean the whole knowledge base first?
No. Start with the 20% of repositories that concentrate most searches, then expand. Duplicates and obsolete versions are filtered by date and status when that metadata exists.
How long does a pilot take?
A pilot on a narrow scope (20,000 to 50,000 documents) ships in 5 to 6 weeks for EUR 12,000 to 20,000. Full rollout follows in 6 to 8 weeks.
Can we use our drawings and scanned documents?
Yes, with an OCR step that adds EUR 2,000 to 8,000 depending on volume. Drawing title blocks and tables need specific tuning to be retrieved well.
Can we switch AI models later?
Yes, if the architecture separates the index from the model. Changing LLM provider then takes 2 to 5 days of work plus a new run of the test set.
Let's scope your project. Send us your document count, the repositories involved and your hosting constraints: we will price a RAG pilot between EUR 12,000 and 20,000, delivered in 6 weeks. Detailed quote within 48 h. WhatsApp +221 77 596 93 33.
Mohamed Bah
Fondateur, Kolonell
Passionate about digital and entrepreneurship in Africa, Mohamed has been helping Sénégalese businesses with their digital transformation since 2020. Founder of Kolonell, he believes every SME deserves a professional and accessible online présence.
