RAG (Retrieval-Augmented Generation) lets AI systems like ChatGPT from OpenAI or Claude from Anthropic answer questions using your specific data - documents, procedures, client history, and internal docs. A standard chatbot only knows what it was trained on; RAG also knows everything you expose to it. Typical implementation: €5,000-€30,000+, with ROI of 5-20 hours per week saved for an average SMB.
How RAG works
Standard AI models know everything up to their training cut-off. They do not know your documents. RAG adds three steps:
- Retrieval - when you ask, the system searches your documents for the relevant parts.
- Augmentation - those parts are passed to the AI as context alongside your question.
- Generation - the AI answers based on your question and the retrieved excerpts.
The result is AI that “knows” your data and answers accurately on that basis.
Where it delivers value
- Internal knowledge base. “How do we process returns?” - RAG retrieves the procedure with sources. Saves 5-15 hours/week. €5,000-€15,000.
- Smart customer support. Searches guides, FAQs, and history to answer 60-80% of inquiries automatically. €8,000-€25,000.
- Sales assistant. “What price did we offer a similar company last year?” - searches quotes, orders, and contracts via CRM/ERP API integration. €10,000-€30,000.
- Legal and compliance. Searches GDPR docs, policies, and opinions to deliver reasoned answers with an audit trail. €15,000-€40,000.
- Technical documentation. Answers “How does our integration with system X work?” from docs, comments, and Slack history. €10,000-€25,000.
What RAG does not do
- It does not make decisions - it informs; the user decides.
- It does not execute actions. For that you need AI agents, often built on frameworks like LangChain.
- It is not perfect. Expect 85-95% correct. Plan for the rest.
- It does not refresh itself when documents change.
Treat RAG as an assistant that accelerates human work, not a replacement.
What drives the cost
Document volume, formats (PDF, Word, Confluence, SharePoint), freshness, privacy requirements (cloud vs self-hosted), user count, personalisation level, and integration scope. Range: €5,000 for a simple setup, €30,000+ for enterprise with strict security. See our cost guide.
Monthly costs: API usage runs €30-€200/month for 200 questions/day, up to €300-€2,000 for 2,000/day. Add €50-€500/month for vector DB and hosting. Realistic SMB total: €100-€800/month.
Privacy paths: enterprise cloud APIs (€15-€60/user/month, GDPR-compliant), self-hosted open-source models (lower quality but data never leaves), or hybrid (sensitive local, generic cloud).
Frequently asked questions
Do we need perfect documentation first? No. Start with what you have. The system is still useful and improves as you add more.
What if RAG gives a wrong answer? A well-designed system cites sources so users can verify, and includes a feedback mechanism for flagging errors that feeds future improvements.
Can we add new documents easily? Yes. Drag and drop into the admin panel; the system indexes and uses them automatically.
How long does implementation take? Small RAG: 4-6 weeks. Mid-sized: 8-12 weeks. Large with many sources: 12-20 weeks.
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