Customer support is undergoing the most significant infrastructure shift since cloud telephony. Legacy IVR systems — built on rigid decision trees, capped at ~30% containment rates — are being replaced by RAG-powered AI agents that read your knowledge base in natural language and serve every channel from the same brain. The economics are decisive: businesses deploying AI call center agents report 60% lower support costs within six months, with customer satisfaction *rising* 15–20% because routine queries resolve instantly and human agents focus on the cases that actually need them.
This pillar collects everything we've published on AI customer support — the macro trend data, the technology that makes it work (RAG over your documents, vector search, channel-agnostic orchestration), the cost models for hybrid AI+human teams, and the architectural difference between "multichannel" (just being present on phone and chat) and "truly omnichannel" (one conversation history, one AI brain, one knowledge base across voice, web chat, WhatsApp, and SMS).
If you're evaluating whether to modernize your support stack, start with the posts below in order. They build from the why (trend data + cost economics) to the how (RAG architecture + omnichannel design) to the implementation reality (LangChain + Qdrant in production, which is linked from the engineering cluster).