AI Support Automation
A 200-person B2B SaaS company was watching support volume climb faster than it could hire. Agents were spending most of each ticket hunting through docs and rewriting the same answers, and customers felt the slowdown. We built them a production support copilot, grounded in their real knowledge and kept under human control, that gave agents their time back without putting the customer relationship at risk.
The challenge
Support quality was still good, but it was getting expensive and slow, and the trajectory was unsustainable.
- 12 minutes per ticket, on average. Most of it spent searching documentation and drafting repetitive replies.
- Volume outpaced hiring. Adding headcount linearly with growth wasn't viable.
- Knowledge was scattered. Answers lived across docs, wikis, and years of past tickets.
- Accuracy was non-negotiable. A wrong automated answer to an enterprise customer was worse than a slow one.
Our approach
We built a retrieval-augmented copilot that drafts grounded, cited responses for agents to review, never an autonomous bot firing replies on its own. Trust came first; speed followed.
- Grounded in real knowledge. Hybrid retrieval over docs and past tickets, so answers cite sources agents can verify.
- Human approval before send. The copilot drafts; the agent edits and approves. Quality stays in human hands.
- Automated triage. Incoming tickets are classified and routed to the right team instantly.
- Evals on retrieval quality. We monitor whether the right context is being pulled, separately from how well the model writes.
How we worked
We started with high-volume, low-risk ticket types where value was easy to prove.
Chunking, metadata, and a refresh pipeline so the copilot never answers from stale docs.
Agents approved every send while we tuned retrieval and tracked accuracy.
Once evals proved reliability, we widened coverage to more ticket categories.
Results
Average handle time dropped from 12 minutes to 4, freeing roughly 38 agent-hours every week for the conversations that genuinely need a human. The system paid for itself in under three months, and customer satisfaction held steady because agents stayed in control of every response.
"Our agents finally stopped copy-pasting from the docs. They spend their time on the hard tickets now, and customers feel it."Director of Support, B2B SaaS
Stack
Support volume outrunning your team?
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