Case study · AI & Automation
An AI support agent that resolves 72% of tickets on its own
HelpDeck's support team was drowning in repetitive tickets. We built an AI agent that reads, resolves, and escalates with guardrails, so humans only touch the cases that need judgment.
Client
HelpDeck
Industry
B2B SaaS
Timeline
8 weeks
Year
2026
0%
tickets resolved end to end
0%
answer accuracy on eval set
0min
median first response time
01 · The challenge
Where things stood
HelpDeck's five-person support team was handling 3,000+ tickets a month, and 70% of them were variations of the same forty questions. Response times had slipped past nine hours, churn surveys started mentioning support speed, and hiring more agents only bought a few months.
Previous chatbot attempts had failed badly. The off-the-shelf bot invented refund policies that did not exist, and the team turned it off within two weeks. Trust in AI inside the company was near zero.
02 · The solution
What we built
We built a retrieval-grounded support agent that answers only from HelpDeck's verified knowledge base, with citations attached to every reply. If confidence drops below a threshold, or the topic touches billing and refunds, the agent drafts a reply and routes it to a human queue instead of sending.
Every action is logged, every answer is scored against a 250-question evaluation set we built with their team, and a weekly quality report goes to the head of support. The agent got smarter and the team could prove it.
03 · The approach
How we did it
Audit the ticket history
We clustered 12 months of tickets to find what actually gets asked, then built the evaluation set from real questions with verified answers.
Ground it in real docs
Help center articles, internal runbooks, and policy pages were chunked, embedded, and indexed with a reranking step for accuracy.
Wrap it in guardrails
Scoped tools, confidence thresholds, forbidden topics, and a human escalation queue with full context attached to every handoff.
Ship, measure, tune
Two weeks in shadow mode answering silently alongside humans, then a gradual rollout with dashboards for resolution rate and accuracy.
04 · The stack
Built with
Chosen for this project, not from a default template. Every build gets the stack its problem deserves.
“Our last chatbot invented refund policies. This one cites its sources, knows when to hand off, and my team finally trusts an AI tool.”
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