Prepare for the conversations
you cannot predict.
Build evaluation datasets for support automation, routing, and service workflows without making real customer conversations the default.
Discuss your use caseCommon tickets only tell part of the story.
Rare intents, ambiguous requests, and sensitive details are easy to miss in a typical evaluation set. Define the conversation patterns you need to test and the boundaries your system must respect.
Intent coverage
Explore uncommon questions, ambiguous language, and mixed intents.
Routing evaluation
Test classification and escalation behavior across controlled scenarios.
Service workflow QA
Validate status changes and handoffs using synthetic case records.
From a challenge
to a dataset.
A support AI team defines billing, account recovery, and escalation scenarios. It reviews synthetic conversation examples for relevance, separates evaluation from training data, and compares routing behavior across versions.
Make the requirements
the starting point.
Agree on the supported data types, generation approach, and evidence needed before a dataset is used.
- An intent and scenario taxonomy
- A review process for generated conversations
- Evaluation criteria for routing and escalation
- A plan to exclude personal customer details
Build the data behind better customer support.
Bring your toughest data problem.
Let’s work out what comes next.