Customer Support AI
24/7 support that actually understands your customers
Overview
A growing B2B software company was struggling to scale their customer support. With 2,000+ customers and only 3 support agents, their average response time had ballooned to 6 hours. Customers were churning, citing poor support as the main reason. The Head of Customer Success reached out after losing two enterprise accounts in one month—both mentioned support responsiveness in their exit interviews.
The Challenge
The support team was drowning in repetitive questions: password resets, billing inquiries, feature how-tos. These made up 70% of tickets but took time away from complex technical issues. Their existing chatbot (rule-based) had a 12% resolution rate—customers hated it and immediately asked for a human. The team needed something that could actually understand context, access their knowledge base, and handle real conversations without making customers feel like they were talking to a wall.
The Solution
I built a RAG-based AI assistant using GPT-4 and LangChain. The system ingests their entire knowledge base (400+ help articles, product docs, past ticket resolutions) into Pinecone for semantic search. When a customer asks a question, the AI retrieves relevant context and generates accurate, conversational responses. It integrates directly with Intercom—handling conversations in the existing support widget. For complex issues it can't resolve, it creates detailed handoff notes for human agents with full conversation context.
The Process
Knowledge Base Audit & Preparation
Exported 400+ help articles, 50 product docs, and 2 years of resolved tickets. Cleaned and chunked content for optimal retrieval. Identified gaps where documentation was missing—worked with their team to fill 23 critical knowledge gaps before launch.
RAG Pipeline Development
Built retrieval pipeline with Pinecone vector database. Implemented hybrid search (semantic + keyword) for better accuracy. Created custom prompt engineering to maintain brand voice and handle edge cases. Added conversation memory so the AI maintains context across messages.
Intercom Integration & Handoff Logic
Integrated with Intercom API for seamless deployment in existing support widget. Built intelligent handoff triggers: sentiment detection (frustrated customers), complexity scoring, and explicit human requests. Handoffs include AI-generated summary so agents have full context.
Testing, Training & Gradual Rollout
Shadow-tested on 500 historical tickets—hit 71% accuracy before launch. Rolled out to 10% of traffic, monitored closely, iterated on failure cases. Expanded to 50%, then 100% over 3 weeks. Built feedback loop where agents flag incorrect responses for continuous improvement.
Tech Stack
“We went from losing customers over support to having it be a competitive advantage. The AI handles the repetitive stuff so our team can focus on complex problems. Customers actually compliment our support now—that never happened before.”
DiaryDot
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