Key takeaways
- AT&T classifies support calls at 91% accuracy and 35% of the prior cost of its previous large-model classifier, using models fine-tuned with H2O Cascade.
- AT&T cut the time to process a day of support-call transcripts from 15 hours to 4.5 hours.
- H2O Cascade distills a large model's skill on a narrow task into small models you own, so classification work does not have to run on an expensive third-party model at scale.
- You test each candidate model on your own transcripts and compare accuracy and cost with the model you run today before any traffic moves.
Every call has a reason, and you pay to find it
Every call your agents take ends in a transcript, and every transcript holds a reason: a billing dispute, a device fault, an outage, a plan change, a threat to cancel. You need that reason tagged the same way every time to route follow-ups, see trends early and fix what drives repeat contacts.
Asking agents to tag calls by hand is slow and inconsistent, and no person applies a large category taxonomy the same way twice. Sending every transcript to a frontier model works in a pilot, but at full volume the bill grows with every call, the overnight batch runs long, and customer conversations leave your environment.