Gladia turns audio into usable transcripts and structured signals. In a signed-in Gladia tab, Strawberry can help a voice-product team review transcription jobs, inspect speaker and language results, and check extracted entities or redactions. It can prepare a precise QA handoff before the transcript reaches a customer system.
It keeps the recording, configuration, and downstream consequence in view, while publishing a result or changing a live integration remains yours to approve.
01
Check the transcript before it becomes somebody else’s source of truth.
A plausible transcript can still mishear a product name, merge two speakers, or turn a key number into the wrong number.
Strawberry can organise the visible job details into a QA pass that points to the exact timestamps, confidence signals, and samples that deserve a human listen.
02
Make audio intelligence settings explainable to the next person.
Language detection, custom vocabulary, diarization, entity detection, and redaction choices are easy to lose once a job has finished.
With the job and its configuration open, Strawberry can document the selected settings, expected behavior, and observed result so a product or compliance review starts from facts rather than guesswork.
03
Hand structured speech data downstream without inventing certainty.
A named entity or sentiment label can be useful in a CRM, warehouse, or product workflow, but it should not be treated as a verified business fact by default.
Strawberry can prepare a field-by-field handoff that distinguishes extracted signals, supporting transcript evidence, and information a team still needs to confirm.
04
Run a recurring Gladia quality pass before a pattern becomes a product problem.
A weekly review is useful when it catches the same accent, vocabulary, channel mix, or recording setup repeatedly producing weak output.
Save the QA checklist as a Gladia-specific Strawberry skill, then schedule a routine to prepare an exception report each week without touching production transcription settings.