A production-grade AI support agent that answers customer queries instantly — trained on real business documents, grounded in facts, and built to hand off to humans when needed.

Every growing business hits the same wall. Customer queries pile up faster than the support team can handle them. The same 30 questions get answered 50 times a day. A customer asks something at midnight and hears nothing until morning. And when a new hire joins support, they spend weeks just learning the product — making mistakes in the meantime.

Hiring more agents doesn't scale. Generic chatbots don't actually help — they match keywords, break on anything slightly different, and frustrate customers more than silence would.

I built a fully custom AI support agent using RAG (Retrieval-Augmented Generation) — a technique where the AI doesn't guess or hallucinate answers, but retrieves the exact relevant section from the business's own documentation before responding.

The result is an agent that sounds like your best support rep, is available 24/7, never gives an off-brand answer, and gets smarter every time you add a new document.

The business uploads their docs — product guides, FAQs, return policies, onboarding materials — in any format. The system chunks, embeds, and stores them in a Qdrant vector database. When a customer asks a question, the agent retrieves the most relevant context and generates a precise, grounded answer using Claude/OpenAI. Every response cites its source. If the question falls outside the knowledge base, the agent says so honestly and escalates to a human — with the full conversation context handed over, so the agent never starts from scratch.

RAG pipeline with Qdrant vector database and semantic search across all uploaded documents

Embeddable chat widget deployable on any website with a single script tag

Smart human handoff with full conversation context preserved on escalation

Admin dashboard to upload new knowledge sources, monitor live conversations, and track resolution rates

Source citations on every response for full auditability

Conversation memory so customers never repeat themselves mid-session

Deployed on AWS EC2 with NestJS backend and Next.js dashboard

Businesses running this system resolve the majority of inbound support queries automatically — without a human touching them. Support teams shift from answering the same questions on repeat to handling only the genuinely complex cases. Response time drops from hours to seconds. And unlike Intercom or Zendesk AI at $500–$1,000/month, this is a one-time build the client fully owns — no platform lock-in, no recurring fees, complete source code delivered.

NestJS · Qdrant · LangChain · Next.js · PostgreSQL · AWS EC2