AI Customer Support Chatbot
OpenAI RAG-powered support chatbot trained on product docs — auto-resolved 80% of support tickets without human intervention within 2 weeks of launch.
The Problem
A B2B SaaS startup with 300+ active customers had a 4-person support team spending 80% of their time answering the same 30 questions. Average first response time was 4 hours during business hours, and zero support after 6pm.
Our Solution
We built a Retrieval-Augmented Generation (RAG) chatbot trained on the company's help docs, knowledge base, and past tickets. GPT-4o answers questions using retrieved context — not hallucinating generic answers. Embedded on the website and WhatsApp, with escalation logic that sends the full conversation to Slack when confidence is low or the user requests a human. An admin dashboard lets the team add articles and monitor escalation rates.
Our Approach
Knowledge Base Audit
Reviewed 200+ support tickets to identify the top 30 question clusters — these became priority content for the RAG pipeline.
RAG Pipeline Build
Ingestion pipeline: scrape docs → chunk → embed (text-embedding-3-small) → store in Pinecone. Nightly re-indexing for new articles.
Chat API & Widget
Streaming chat API in CodeIgniter 4 (SSE) and Alpine.js chat widget with pre-chat gate to capture user email for context.
Escalation & Slack Integration
Confidence scoring + Slack notification with full conversation thread so support agents can jump in directly.
A/B Testing & Tuning
Ran 2 weeks of A/B: generic GPT-4o (40% resolved) vs RAG (80% resolved). Shipped RAG, tuned the system prompt.
The Results
| Metric | Before | After |
|---|---|---|
| Tickets Auto-resolved | ~20% | 80% |
| First Response Time | 4 hrs avg | < 10 seconds |
| Support Coverage | 9am–6pm only | 24/7 |
| Monthly Support Cost | Baseline | 50% reduction |
Tech Stack
Have a Similar Challenge?
Tell us your problem and we'll map out a solution — free consultation, no commitment.