A practical framework for deploying AI-powered customer support chatbots, covering the architectural decision between rule-based and LLM-backed systems, RAG pipeline design (chunking strategies, embedding, vector stores), a 4-category automation boundary framework, escalation trigger design, and a 6-phase implementation timeline. Key benchmarks from real deployments: 55.68% ticket deflection, escalation rate reduced from 41% to 17%. Also compares SaaS platforms (Intercom Fin, Zendesk AI) against custom builds using Netguru's Chatguru product, with guidance on confidence threshold tuning, sentiment-based escalation, and post-launch observability with Langfuse.
Table of contents
TL;DR: Framework and key benchmarksRule-based vs AI-powered chatbots: The architectural forkHow a production support chatbot works: Intent, RAG, LLM layerThe 4-category automation framework: What to hand to the botEscalation architecture: Triggers, handoff design, and sentiment signalsBuild vs buy: Chatguru custom build against SaaS platformsImplementation steps: From intent taxonomy to pilot rolloutPost-launch observability, monitoring, and securityFrequently asked questionsReady to define your automation boundary?Sort: