✨ TLDR
Built an AI copilot for the support team at a hypergrowth startup (valued at $10B+). The agent takes a support ticket, searches docs and past resolutions, and drafts a response.
Went from 0% automation to ~80% resolution rate.
🎯 The Problem
Imagine being the support team for a company in hypergrowth mode. User base exploding. Thousands of questions pouring in. Every day.
Before I came in:
- Everything was manual. No automation, no tooling.
- Same questions kept appearing. Already answered in docs or past tickets.
- Support Agents spent more time researching than helping. Context was scattered.
💡 The Approach
Build a copilot, not an autopilot.
The goal wasn’t to auto-respond to users. That’s risky for a company of this scale. Instead, I built a copilot that:
- Takes a support ticket ID
- Parses the user’s question and context
- Searches product documentation for relevant answers
- Searches past resolved tickets to find similar issues
- Drafts a response for the agent to review and send
The agent does the research. The human makes the call.
⚡ Shipping Fast
I got something working fast, put it in front of the team, and improved based on real usage.
This is how internal tools should work — ship, learn, iterate. The first version was rough. The latest version was hitting 80% resolution.
📊 Results
| Metric | Before | After |
|---|---|---|
| Resolution automation | 0% | ~80% |
| Time-to-first-response | Manual research | Near-instant draft |
| Agent workload | Researching everything | Focus on complex issues |
The support team went from drowning in tickets to having breathing room for the hard problems.
🧠 What I Learned
Copilot > Autopilot for support. Copilots keep humans in the loop while still saving 80% of the work.
Search quality is everything. The agent is only as good as its retrieval. If it can’t find the right docs or past tickets, the draft is useless. I spent most of my iteration time on setting up the right tools for the agent.
Internal tools are underrated. Companies invest millions in customer-facing products but neglect internal tooling. A few days of work can unlock massive leverage for internal teams.
🔗 Related
- AI Consulting — More about my consulting work