Use case
Bad Thread Clusters
Clusters of failed or low-quality threads, grouped by failure pattern, with example threads and a recommended owner — the unit of investment in conversation intelligence.
Overview
Every support team has a top 10 list of recurring failure patterns. Clustering makes that list explicit, ranked, and actionable.
What it solves
Replaces 'we have a lot of tickets about X' with a tracked cluster, a count, a trend, and a named owner.
How we build it
Embedding similarity seeds clusters; LLM-assisted labeling refines them; humans review and merge. Each cluster has identity, count, trend, example threads, a hypothesized cause, and a recommended owner. Closing the cause shrinks the cluster.
- Embedding-seeded, LLM-refined clusters
- Identity, count, trend per cluster
- Hypothesized cause and recommended owner
- Cluster shrinkage as success metric
What changes when it is in place
Failure patterns become visible and ownable. Investment becomes targeted.