Real-Time Transcription

Deepgram turns live audio streams, like a support call in progress, into text as the conversation happens. A Beam agent reads the streaming transcript, applies the account's rule for what to do with certain phrases or keywords, for example flagging a cancellation request the moment it's said, and notifies the relevant person or updates a record while the call is still live. Passages the model marks as low confidence, or audio too noisy to transcribe reliably, get set aside for a human to confirm afterward. This lets a rule act on what's being said without waiting for the call to end.

Recorded Audio Transcription

Deepgram can transcribe a batch of recorded files, such as a stack of stored support calls or voicemails, without anyone listening to them first. A Beam agent reads each finished transcript, checks it against the account's rule for words or topics that matter, for example a compliance phrase or a competitor name, and updates a record or notifies a team when a match turns up. Files that come back with a low confidence score across most of the transcript, or in a language the rule doesn't cover, are left for a person to listen to directly, rather than piling up unchecked.

Speaker Labels and Keyword Spotting

Deepgram can separate a transcript by speaker and flag defined keywords as they come up in a conversation. A Beam agent reads who said what and checks it against the keywords the account's rule cares about, like a product name or a complaint phrase, then updates a record or notifies a person when a match lands on the right speaker's line. Ambiguous speaker splits, like overlapping voices, or a keyword match with no clear context, are left for a human to confirm before anything gets acted on. This keeps action tied to who actually said something, not just that it was said.