AI Support Agent Stack
Tier-1 support handled by an AI agent grounded on your docs, with human handoff.
/ quick answer
Cover ingestion, retrieval, agent runtime, chat UI, and observability for a production support bot. Tier-1 support handled by an AI agent grounded on your docs, with human handoff.
- Intercom Fin or Plain (chat UI + handoff)
- Supabase pgvector or Pinecone (vector DB)
- OpenAI or Anthropic (model)
- LangSmith or LangFuse (evals + traces)
- Notion or GitBook (source of truth for docs)
- Make (sync docs → vector DB on update)
- Zendesk AI instead of Intercom Fin
- Weaviate instead of Pinecone
/ frequently asked
What is the AI Support Agent Stack stack for?
Cover ingestion, retrieval, agent runtime, chat UI, and observability for a production support bot.
Which tools are in this stack?
Intercom Fin or Plain (chat UI + handoff), Supabase pgvector or Pinecone (vector DB), OpenAI or Anthropic (model), LangSmith or LangFuse (evals + traces), Notion or GitBook (source of truth for docs), Make (sync docs → vector DB on update).
Are there alternatives to this stack?
Yes — Zendesk AI instead of Intercom Fin, Weaviate instead of Pinecone.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →Agentic Workflow
A workflow where an LLM decides the next step instead of a hard-coded path.
Related workflows
Turn this into a repeatable process.
- →Build an Internal Knowledge Bot
Ship a Slack bot that answers questions from your company docs.
- →RAG Content Ingestion Pipeline
Convert messy docs into searchable, cited knowledge chunks for AI systems.
- →Build a Tier-1 Customer Support Agent
An agent that handles common tickets end-to-end and hands off the rest.
- →AI Slack Digest for Busy Teams
Ship a morning digest of what mattered in Slack yesterday.
Related tool stacks
The tools that run it in production.
- →RAG Context Enrichment Stack
A technical stack designed to enrich the contextual data provided to a Retrieval Augmented Generation (RAG) system, improving the quality and depth of LLM responses.
- →Low-Cost RAG Stack
This stack combines open-source and cost-efficient components to build a Retrieval-Augmented Generation (RAG) system with minimized operational expenses.
- →Agent Architecture Stack
The minimum tooling to design, run and observe a production agent.
- →Multi-Agent Orchestration Stack
Tooling for coordinating several specialised agents with reliable handoffs.
Related use cases
How people apply it, and what came out.
- →E-commerce Brand Automates 70% of Support Tickets
A DTC brand deployed a RAG support agent over policies, FAQs, and order data.
- →Support Team Replaces Wiki Sprawl With a Knowledge Graph
A support org connected policies, playbooks, tickets, and RAG answers into one system.
- →Ecom Store Cuts Support Tickets 40% With Agent
A DTC brand deflected 40% of tickets with a grounded AI agent — CSAT went up, not down.
- →Coach Automates Onboarding, Doubles Client Count
Executive coach removes the 4h admin tax on every new client.