Entity detection
Extract structured references such as people, organizations, places, dates, and domain-specific values from transcripts.
Entity detection converts important spans in a transcript into structured data that applications can search, route, or validate.
Common entity classes
General-purpose pipelines may detect people, organizations, locations, dates, times, quantities, and monetary values. Domain-specific extraction can target identifiers or concepts meaningful to your application.
Preserve provenance
Useful entity output should remain connected to the source transcript span and, where available, audio timing. This makes it possible to review the evidence rather than accepting a detached value.
Validate consequential values
Speech and extraction can both be uncertain. Validate identifiers, amounts, medical details, or other consequential fields before triggering irreversible actions.
For schema-shaped application output, combine entity detection with LLM processing.
Transcript formatting
Turn raw ASR output into readable transcripts: punctuation, casing, numbers, and paragraphing that stay stable for people and downstream systems.
Sentiment analysis
Add conversation-level or segment-level sentiment signals to a speech pipeline, scored from the transcript alongside speakers and entities.