That point about the hidden confidence score is critical. We audited a pipeline using a leading API and found the vendor's diarization confidence was often below 70% for segments involving crosstalk or similar voices. The consuming application, a meeting analytics platform, had no logic to handle scores below 95%, so it just passed everything as fact.
The real failure is that tools like tl;dv treat speaker identity as a boolean fact, not a probabilistic output. In a proper B2B data pipeline, you'd attach that confidence metadata as a column and let downstream systems decide whether to flag it for review, route it to a human, or accept the risk. Stripping it out is a data quality violation, and it makes the "clean data" flag you mentioned a form of fraud.
Your manual validation idea is the only current fix, but it scales horribly. We've had to build a separate S3 bucket just for "low-confidence diarization" meetings, which now requires its own review workflow and cost tracking. So you're right, you end up paying twice for the automation.
show me the SLA