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Home / Pricing / AI Integration

AI integration cost

A 2026 pricing guide - chatbot, voice agent, and full AI pipeline.

Updated:

AI implementation cost

The cost of AI integration depends on the type of solution, complexity of conversational scenarios, required integrations, and usage scale. A simple FAQ chatbot is a very different investment than a multi-agent automation system with predictive analytics.

In addition to the implementation cost, consider monthly operational costs - API tokens, hosting, and monitoring. In a typical case, the ROI from AI implementation is achieved in 3-6 months through reduced customer support costs. See how AI integration delivery works at Midapt.

How much does it cost?

AI Chatbot

EUR 1,900 - 6,000

Intelligent chatbot on your website or messenger - customer support, FAQ, recommendations.

  • GPT-4 / Claude integration
  • Knowledge base (RAG)
  • Website widget
  • Conversation history
  • Management panel
  • Delivery in 3-6 weeks
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AI Voice Agent

EUR 6,000 - 19,000

Voice AI agent for phone handling - bookings, FAQ, routing, follow-up.

  • Speech-to-Text + Text-to-Speech
  • VoIP PBX integration
  • Conversational scenarios
  • Agent handoff
  • Call analytics
  • Delivery in 4-8 weeks
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Full AI Pipeline

EUR 19,000 - 47,000+

Comprehensive AI system - process automation, predictive analytics, multi-agent.

  • Multi-agent architecture
  • CRM/ERP integration
  • Workflow automation
  • Predictive analytics
  • Model fine-tuning
  • Delivery in 2-6 months
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What affects the price?

AI Model Choice

GPT-4, Claude, Llama, Gemini - each model has a different per-token price. Open-source models reduce operational costs.

Knowledge Base (RAG)

Document volume, data format, and required answer quality affect the complexity of the RAG implementation.

Integrations

VoIP, CRM, ERP, databases - each integration requires data mapping and edge case testing.

Operational Costs

API tokens, GPU hosting, monitoring - monthly AI maintenance costs range from EUR 120 to EUR 1,200 depending on scale.

Data Security

Processing personal data requires encryption, anonymization, and GDPR compliance.

Training and Optimization

Fine-tuning, prompt engineering, and iterative quality optimization is an ongoing process.

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