A Support Ops Guide to Inventing Work
How Support Ops can find recurring problems, spot missed customer needs, and turn those signals into changes that improve support.
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Small questions, measurable gains with Jev
Two small evals found higher recall for ticket insights and lower cost and latency for signal grouping.
Keeping AI answers grounded in the help center you trust
Check whether AI answers apply the right guidance, distinguish content gaps from answer errors, and review conversations after an article change.
Getting more from Fin analytics with conversation evidence
Turn a change in Fin's results into a specific follow-up by reviewing comparable conversations and checking what changed for customers.
Support quality assurance when AI handles most of the volume
Build an AI support QA process with clear review criteria, sensible sampling, and a way to check whether fixes worked.
Chatbot analytics that connect the score to the conversation
Choose useful chatbot metrics, compare like-for-like results, and use conversation evidence to decide what to improve.
What else to look at when you have a strong deflection rate
Deflection is useful. Conversation evidence explains whether a closed AI support conversation was a good outcome for the customer.
Introducing CraftCX
CraftCX helps support and product teams review AI support quality and find customer problems in the conversations their agents handle.