Intelligent Support RAG Bot & Ticket Auto-Triage
An AI-powered customer support copilot engineered with vector embeddings and strict medical compliance guardrails. The system ingests technical documentation and policy guides, answers complex inquiries with precise source citations, and routes unresolved tickets to the appropriate human team with summarized context.
Business & Technical Friction
- Support team overwhelmed by high volumes of repetitive regulatory and technical queries.
- Strict data privacy requirements forbidding PHI (protected health information) storage in LLM memory.
- Need for deterministic fallback and zero hallucination risk on clinical policies.
Architecture & Implementation
Built a secure Python RAG pipeline using Pinecone vector indexing and OpenAI embeddings. Implemented strict prompt sandboxing, automated PII sanitization filters, and Zendesk webhook escalation.
Core Technologies Deployed:
Engineered Features & Workflows
Key operational capabilities built into this platform to solve the core business challenges.
Deterministic Knowledge Retrieval
Semantic search retrieving exact policy paragraphs with linkable source verification.
Automated PII/PHI Anonymization
Pre-processing filter scrubbing patient identifiers, emails, and phone numbers before LLM inference.
Contextual Ticket Escalation
Automatically formats customer query history and suggested resolution for human agents.
Continuous Accuracy Telemetry
Admin analytics dashboard tracking query sentiment, thumbs-up/down feedback, and coverage gaps.
Measured Impact & Performance
64% first-contact resolution on standard technical and policy support queries.
Reduced average initial ticket response time from 4.5 hours to under 30 seconds.
Zero data privacy breaches or unauthorized data retention.
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