Speaker diarization and transcription models are good, but not perfect or production-ready out the box. Kili's audio annotation tool gives you the interface where domain experts and HITL teams can correct these models and improve them at scale. Launch 100+ projects fast without compromising quality and security.
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A misplaced speaker boundary doesn't just read wrong. It teaches your model that the customer said what the agent said. Kili puts the automated first pass in front of the people who know the domain, so they correct speaker attribution and wording against the waveform, including multi-party conversation and overlapping speech, without waiting on an ML engineer. What you train, fine-tune, and evaluate on is a conversation your experts have verified turn by turn.
AI teams using Kili Technology run hundreds of audio annotation projects across multiple use cases. Volume is usually where training data quietly degrades: two reviewers disagree on where a turn began, a third drifts over a long project, and the model inherits all of it. Consensus scoring, gold-standard testing, and multi-stage review put a number on every asset and every contributor, so the thousandth hour of audio is labelled to the same standard as the first.
The calls most worth training on are the ones carrying PHI, financial detail, and personal data. Run Kili wherever your security policy requires: managed SaaS, an Azure Marketplace app in your own subscription and region, a hybrid mode where raw audio never leaves your storage, or fully on-premise with no internet dependency after installation. Every tier is covered by GDPR, SOC 2 Type II, ISO 27001:2022, and HIPAA.
Kili Technology is a complete data suite that supports all data types and handles specialized formats for domain-specific requirements.
You can also check out our documentation to learn more about our features. We're ready when you are.
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Learn how Kili Technology has changed the way these teams train, fine-tune, and evaluate their models.
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