Zapier's approach to MCPs offers more granular control than individual providers

2026-02-06 · Source: How I AI · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Data Science & Analytics · Depth: Intermediate, quick

Summary

Zapier has implemented an internal Retrieval Augmented Generation (RAG) chatbot to enhance customer responsiveness and internal information retrieval. This chatbot, designed for internal use by teams like sales and product marketing, allows employees to quickly search for customer feedback and use case patterns by querying various internal databases. Additionally, Zapier utilizes a similar RAG system externally, providing an end-user-facing chatbot. This external version leverages knowledge sources such as help documentation and a Google Sheet-like table to answer customer inquiries, demonstrating a dual application of the RAG architecture for both internal efficiency and external customer support.

Key takeaway

For AI Architects designing customer support systems, you should consider a dual RAG chatbot strategy. Implementing an internal RAG system can significantly boost team efficiency by centralizing access to customer feedback and use cases, while a parallel external RAG chatbot can provide immediate, accurate support to end-users, reducing reliance on human agents for common inquiries.

Key insights

RAG chatbots enhance both internal information retrieval and external customer support by centralizing diverse data sources.

Principles

• Internal RAG improves team responsiveness.
• External RAG supports end-user queries.

Method

Implement a RAG chatbot fed by internal databases for employee queries and an external RAG chatbot using help docs and structured data for customer support.

In practice

• Build an internal chatbot for sales/PMM.
• Integrate help docs into an external chatbot.

Topics

• RAG Chatbot
• Customer Responsiveness
• Knowledge Management
• Internal Tools
• AI Applications

Best for: AI Architect, AI Product Manager, Product Manager, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by How I AI.