AI Support Agent for Businesses
An autonomous customer support agent capable of resolving complex technical queries by reading company documentation.
Role
Lead Engineer & Architect
Timeline
6 Weeks
Status
Production
Project Type
Full-Stack AI System
Tech Stack
The Problem
Customer support teams were overwhelmed by repetitive, tier-1 technical queries. Existing keyword-based chatbots were rigid, leading to frustrated users and a high escalation rate to human agents.
The Solution
Designed and built a Retrieval-Augmented Generation (RAG) pipeline that embeds Zendesk articles, internal Notion docs, and product manuals into a vector database. The agent dynamically retrieves this factual data to generate highly accurate, contextual answers, completely eliminating hallucinated responses.
System Architecture
How the data flows from the user to the core engine and back.
Core Features
What was specifically engineered for this system.
Semantic similarity search across 10,000+ documents
Real-time streaming responses via WebSockets
Conversation history & memory management
Admin dashboard for reviewing low-confidence answers
Automated syncing with Zendesk knowledge base
Role-based access control and rate limiting
Technical Decisions
FastAPI Backend Integration
Chose FastAPI due to native async support and seamless integration with Python's AI ecosystem.
Pinecone Vector Storage
Selected Pinecone for managed infrastructure and sub-millisecond query latency.
Challenges Solved
Response Accuracy
LLMs naturally hallucinate. Solved by tuning the RAG prompt to strictly say 'I don't know' if context wasn't found in the vector DB.
Data Synchronization
Company docs changed daily. Built a nightly cron job that re-embeds updated articles without causing downtime.
Fast User Experience
LLM generation is slow. Implemented Server-Sent Events (SSE) to stream tokens directly to the UI, reducing perceived latency to under 500ms.
Expected Outcomes
60%
Expected Reduction in Ticket Volume
<500ms
Target Perceived Latency
99.9%
Target System Uptime
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