The Problem
A B2B SaaS platform was drowning in tier-1 support tickets — password resets, billing queries, onboarding questions. Two full-time support staff were spending 80% of their time on repetitive queries that followed the same patterns every single week.
Average response time was 4 hours. Customer satisfaction was sliding. The support team was burning out on work that added no real value to either the business or the customers they served.
They came to us with a straightforward brief: reduce the load on the support team without degrading the customer experience. Our answer was a custom AI agent — not a generic chatbot, but a purpose-built system trained on their specific product, their specific docs, and their specific history of resolved tickets.
Weekly Support Ticket Volume
8 weeks before and after AI agent deployment
What We Built
The architecture was built on LangChain + GPT-4o with a retrieval-augmented generation (RAG) layer over three data sources: their product documentation, 18 months of resolved support tickets, and their changelog.
The agent integrated directly into Intercom using their webhook API. Every incoming conversation was first classified by intent. Tier-1 queries (account issues, billing FAQs, how-to questions) were handled autonomously. Tier-2 queries (bugs, edge cases, escalations) were routed to the human team — along with a full context summary of what the agent had already established.
“The agent doesn't just answer — it reads the conversation history, checks the user's account status via API, and responds with context-aware answers. It's not a FAQ bot. It's a trained support engineer.”
The Results
60%
support volume handled by AI
47s
average AI response time
+18pts
NPS increase in 90 days
0
new hires during 35% user growth
Within 90 days of going live, 60% of all incoming support conversations were being handled entirely by the AI agent — with no human involvement. Average response time dropped from 4 hours to 47 seconds.
The two support staff were reassigned to proactive customer success work. NPS improved 18 points. The company stopped hiring for additional support headcount despite 35% user growth during the same period.
Key Lessons
Train on YOUR data, not generic data
A GPT wrapper answering from generic knowledge gives generic answers. The quality comes from fine-tuning on your actual ticket history and product documentation.
Always design the escalation path first
The agent handles exactly what it should handle and nothing more. Clear confidence thresholds mean customers are never stuck with an AI that can't help them.
Monitor confidence scores weekly
The first 30 days of deployment revealed categories the agent was uncertain about. Weekly review of low-confidence responses drove a 22% accuracy improvement through targeted retraining.
Want this for your business?
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