Audio submission to the API

Rev.ai exposes speech-to-text through an API that accepts audio for recognition. A Beam agent reads a request to transcribe a recording, checks the file and language settings against your rule, and passes the audio to the API where the request is valid. It records that the job started and its identifier. Files that are oversized, in an unsupported language, or marked sensitive are held for a person to approve first. Routine submissions run on their own, so applications get their audio processed without a developer wiring up each call and handling every response by hand.

Job status polling

Rev.ai works through each submitted job and reports when it is finished. A Beam agent reads the status of outstanding jobs, applies your rule for waiting, retrying, and timing out, and moves each one forward as its state changes. Completed jobs are marked ready and passed to the next step, while failures are retried within limits. Jobs stuck far beyond their expected time, or failing repeatedly, are escalated to a person to look into. The agent keeps tabs on many jobs at once, so teams are not refreshing a queue to see which transcriptions have come back.

Transcript retrieval and storage

Rev.ai returns the finished text once recognition completes. A Beam agent reads the completed transcript, applies your rule for where it belongs and how it should be tagged, and writes it to the destination system against the source recording. It records the length and confidence details the API reports. Transcripts with very low confidence, or output that looks garbled or empty, are routed to a person to check before anything relies on them. Dependable results are stored on their own, so downstream systems get clean text and only questionable jobs wait for a human eye.