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Case studies

Three AI workloads, the problems we found, and the changes that made them work better.

Teaching a CRM agent to finish the job

We wanted a lower-cost model to handle everyday CRM work: updating contacts, creating opportunities, and qualifying leads. The test was whether it could leave the right changes in the CRM.

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75.5%

lower model cost than Sonnet 4.6

Cutting response times for everyday operations

A small model could handle scheduling and incident tasks, but spent too long generating its answers. We investigated what it was writing and found a way to make it much faster.

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5.2×

faster median model response than Sonnet 4.6

The small model passed every task check on 92.2% of attempts; Sonnet passed 100%. Both ran 30 AutomationBench workflows, with 90 attempts per model.