Deflecting half the ticket queue for a SaaS company. 54%.
of tickets deflected. An AI support layer reading docs and account data deflected 54% of tickets, cut first-response time 60%, and let support scale with the customer base instead of headcount.
Book a Fusion WorkshopWhere the time was going.
As the customer base grew, the ticket queue grew with it, and most tickets were 'how do I' questions already answered somewhere in the docs. Support was effectively a live search engine for the knowledge base.
First-response times slipped as volume rose, which dinged CSAT even when the eventual answers were good.
The knowledge base existed but was underused because customers found it faster to just open a ticket.
What we shipped.
We put an assistant in front of the queue that actually reads the docs and the customer's own account state.
What changed.
Support scaled with customers, not with headcount.
The assistant deflected 54% of tickets and cut first-response time 60%, lifting CSAT by 22 points. Agents moved to the complex tickets that need product knowledge, and the docs got measurably better as a side effect.
“Our support team finally works on the hard problems, and customers get instant answers to the easy ones.”
VP Customer Success, SaaS company
Built with, and what you own.
The company owns the assistant configuration, the knowledge base, and the analytics, running on its own helpdesk and product data.
Helpdesk·Knowledge base·Product API·AI assistant·Analytics
Questions about this build.
Does it make up answers?
No. It answers from your live docs and account data with citations, and escalates when it is not confident.
Does it get better over time?
Yes. Unanswered questions are surfaced to improve the docs, which raises deflection without extra agent effort.
Other builds.
Cutting no-shows for a multi-location dental group·Capturing every after-hours call for an HVAC contractor·Sub-minute lead response for a real estate team·Scaling support without scaling headcount for an online retailer