The verdict in three sentences
An AI document search engine (internal RAG) over your thousands of files costs 20,000 to 50,000 EUR in Berlin in 2026, delivered in 8 to 12 weeks. The gain is measured in 3 to 5 hours per week per employee who stop hunting for information across scattered folders. The structuring issue is confidentiality: a sovereign or EU-hosted LLM changes the quote but protects your sensitive data.
Breaking down the price of an internal RAG engine
Unlike a simple chatbot, an enterprise search engine must index heterogeneous sources (SharePoint, Drive, ERP, PDF, email) and manage access rights per user.
| Item | 2026 range (EUR) | Type |
|---|---|---|
| Scoping + source mapping | 3,000 - 6,000 | One-off |
| Indexing + ingestion pipeline | 6,000 - 10,000 | One-off |
| Embeddings + vector database | 4,000 - 7,000 | One-off |
| RAG engine + search interface | 6,000 - 15,000 | One-off |
| Access rights (ACL) per document | 3,000 - 8,000 | One-off |
| Testing, UAT, go-live | 2,000 - 5,000 | One-off |
| Project total | 20,000 - 50,000 | One-off |
| Sovereign hosting + vector DB | 300 - 900 / month | Recurring |
| LLM API (internal usage) | 0.002 - 0.01 / request | Recurring |
| Re-indexing + maintenance | 500 - 1,500 / month | Recurring |
The access rights (ACL) item is often underestimated: an employee must only see documents they are entitled to, otherwise the engine becomes a security hole.
The hidden cost: time lost searching
Before comparing, let's quantify the problem. According to 2026 studies, a knowledge worker spends on average 1 to 2 h/day searching for or recreating information that already exists.
| Scenario | Without AI engine | With AI engine |
|---|---|---|
| Employees affected | 40 | 40 |
| Search time/week/person | 5 h | 1.5 h |
| Hours lost/week (total) | 200 h | 60 h |
| Hours recovered/week | — | 140 h |
| Avg loaded hourly cost | 35 EUR | 35 EUR |
| Value recovered/week | — | 4,900 EUR |
| Value recovered/year (46 wks) | — | ~225,000 EUR |
Even dividing this figure by three to stay conservative, a 40,000 EUR project pays for itself in less than a quarter.
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Mini case study
Thomas, CIO of a mid-sized consulting firm in Berlin (180 staff, including 40 consultants on client engagements), notices his teams constantly recreate deliverables already produced. He invests 38,000 EUR in an internal RAG engine wired to SharePoint and his DMS, with per-engagement ACL. Across the 40 consultants, search time drops from 5 h to 1.8 h/week, i.e. 128 h recovered/week. At 40 EUR/h loaded, that is 5,120 EUR/week, roughly 235,000 EUR/year. Even counting only 30% of that gain, the project pays back in under 2 months. Running cost: 1,400 EUR/month.
FAQ
Will my confidential data go to an AI vendor? Not if the engine is built with an EU-hosted or sovereign LLM and a vector database on your side. The premium for sovereign hosting is 15 to 30% of the running budget, to weigh against your sensitivity.
How many documents can it index? From a few thousand to several million. Indexing cost grows with volume and format heterogeneity (scanned PDFs, spreadsheets, emails), hence the importance of initial scoping.
What is the implementation timeline? 8 to 12 weeks: 2 to 3 for source mapping, 4 to 6 to build the pipeline and engine, 2 to 3 for UAT and phased rollout.
Does the engine respect existing access rights? Yes, via an ACL layer that filters results per user. It is a distinct line item in the quote (3,000 to 8,000 EUR) that should never be cut.
Does it need regular re-indexing? Yes: re-indexing (500 to 1,500 EUR/month) ensures the engine answers on up-to-date documents and not stale versions.
Let's scope your project. Tell us your document sources (SharePoint, Drive, ERP, DMS), the number of employees affected and your sovereignty requirement: we'll price the engine and the expected time savings. 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.
