LLM Integration: Automating 70% of Customer Support Queries

Overview

A growing SaaS company was struggling to scale their customer support. Response times were increasing, costs were ballooning, and customer satisfaction was declining. They needed an AI-powered solution that could handle routine queries while maintaining quality and knowing when to escalate to humans.

The Challenge

• Average response time of 4+ hours for support tickets
• 70% of queries were repetitive questions answerable from documentation
• Support costs growing faster than revenue
• Inconsistent answer quality depending on which agent responded
• No 24/7 coverage, leading to poor experience for global customers

The Solution

I designed and built an AI-powered support system using modern LLM techniques:

• Implemented RAG (Retrieval Augmented Generation) using company documentation, FAQs, and past ticket resolutions
• Built intelligent query classification to route complex issues to humans
• Created a confidence scoring system - low confidence queries escalate automatically
• Developed a feedback loop where agent corrections improve the model
• Integrated with existing helpdesk software for seamless workflow

Tech Stack

Key Learnings

• 1RAG is essential - LLMs alone hallucinate too much for support
• 2Build clear escalation paths - AI should know its limits
• 3Measure not just automation rate but answer quality
• 4Continuous improvement through human feedback is critical