The verdict in three sentences
A support chatbot trained on your internal data (RAG) only pays off if it cites its sources and can say "I don't know": without guardrails, it erodes trust. In London in 2026, the project costs 12,000 to 40,000 EUR, runs for 100 to 800 EUR/month and ships in 6 to 10 weeks. Well scoped, it deflects 40% of tier-1 tickets and pays back in under 9 months.
What a RAG chatbot costs in 2026
Budget depends on documentation volume, number of connectors and the demanded level of guardrails. Here are the observed tiers.
| Tier | Budget (EUR) | Scope | Monthly run |
|---|---|---|---|
| Essential | 12,000 – 18,000 | 1 knowledge base, web widget | 100 – 250 EUR |
| Standard | 18,000 – 28,000 | + CRM, history, human escalation | 250 – 500 EUR |
| Advanced | 28,000 – 40,000 | + multichannel, WhatsApp, analytics | 500 – 800 EUR |
The run covers LLM tokens, the vector database and hosting. A volume of 3,000 to 8,000 conversations/month stays in the 250 – 500 EUR range for a well-chosen model.
ROI through ticket deflection
Value comes from saved agent time. Here is a projection for three support volumes typical of a London SMB.
| Tickets/month | Tier 1 (55%) | 40% deflection | Hours saved | Savings/month (32 EUR/h) |
|---|---|---|---|---|
| 600 | 330 | 132 | ~44 h | 1,408 EUR |
| 1,200 | 660 | 264 | ~88 h | 2,816 EUR |
| 2,000 | 1,100 | 440 | ~147 h | 4,704 EUR |
| 3,500 | 1,925 | 770 | ~257 h | 8,224 EUR |
We assume ~20 minutes of human handling per tier-1 ticket. Even at the lowest volume, annual savings (16,896 EUR) far exceed an Essential project.
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Mini case study
Marc, customer care lead at a B2B SaaS vendor in London, gets 1,200 tickets/month, 55% tier 1. He picks the Standard offer: 24,000 EUR, run 400 EUR/month. A 40% deflection saves ~88 agent hours/month, i.e. 2,816 EUR/month (33,792 EUR/year). Net of the run (4,800 EUR/year), the annual gain reaches 28,992 EUR. The investment pays back in about 10 months, and his agents refocus on high-value tickets, lifting CSAT.
FAQ
Can the chatbot make up answers? With a properly built RAG, it only answers from your documents and cites sources; if it can't find an answer, it escalates to a human. This guardrail separates a reliable project from a risky demo.
What automatic resolution rate should I target? A realistic goal is 35 to 50% of inbound requests in year 1, focused on tier 1. The rate rises as the knowledge base absorbs unresolved cases.
Do we have to rebuild our knowledge base? Not entirely, but a cleanup is often needed: stale articles, duplicates and unstructured pages degrade answer quality. This framing is 25 to 35% of the budget.
How long for the rollout? Count 6 to 10 weeks: indexing, guardrail tuning, testing on your real conversations, then a gradual go-live with human escalation.
Can it plug into WhatsApp? Yes, the Advanced offer includes multichannel (web, WhatsApp, email). It's relevant if a large share of your customers already contact you by messaging.
Let's scope your project. Share your monthly ticket volume, your channels and the state of your knowledge base: we cost the deflection and ROI. 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.
