Problem Statement
High support volumes
Generic chatbot responses
Long resolution times
Low customer satisfaction
Solution Overview
Built an LLM-powered support automation engine with:
Intent detection
Contextual grounding (RAG)
Workflow automation
CX agent escalation
Steps to Build
1. Model Selection
Evaluated GPT-4, Claude 3, LLaMA 3.
Selected GPT-4 for task reasoning + multi-step decision-making.
2. Workflow Design
User query → classify intent → retrieve knowledge → generate response
If confidence < threshold → escalate to human agent
3. RAG Pipeline
Embedded product FAQs, support tickets, onboarding docs
Used metadata filtering for faster retrieval
4. Edge-Case Testing
Ambiguous questions
Contradictory product states
Safety boundaries (refunds, compliance, PII)
5. Deployment
Integrated with Zendesk & Intercom
Added conversational analytics + weekly retraining dataset creation
Business Impact
65% automation for Tier-1 queries
30% reduction in support costs
Faster resolution times → +18 NPS improvement