Turn recorded conversations into data
Tavnit Signals turns audio recordings into a flat table. It separates speakers, splits a recording into its distinct conversations, classifies each one, and evaluates your rules and extraction fields turn by turn — so a day of calls becomes rows you can filter rather than hours you have to listen to.
Why this is painful
Conversations are where most of what a company learns actually happens, and almost none of it is captured. Recordings exist, but reviewing them means listening, so in practice a manager samples a handful and generalises from those.
Where QA does happen, it is a person with a scorecard filling in a form after each call — expensive, inconsistent between reviewers, and covering a fraction of the volume.
What to extract
| Field | Why it needs care |
|---|---|
| Interaction type | One recording often contains several conversations. Each is classified — sale, support, return — or marked as other. |
| Speaker role | Turns are attributed to expected participants such as salesperson or customer; unexpected voices are labelled other. |
| Turn-level rules | Yes/no checks applied to every turn — was the promotion mentioned, was the disclaimer read. |
| Extraction fields | Data pulled from each turn, such as products mentioned or objections raised. |
| Content categories | Each turn labelled — objection, question, problem, solution, appreciation — so patterns are countable. |
| Conversation outcome | Judged once over the whole conversation: was the sale closed, was the issue resolved. |
What makes call analytics hard
One recording is not one conversation
A shift recording from a counter or a support line contains many separate exchanges. Treating the file as a single conversation produces averages that describe nothing. Recordings are split into distinct interactions first, each classified and evaluated on its own — the audio equivalent of splitting a multi-document PDF.
Turn-level and conversation-level questions are different questions
'Was the discount mentioned' is asked of each turn and can be true several times. 'Was the sale resolved' is asked once of the whole exchange. Conflating them gives you a metric that counts mentions when you wanted outcomes, so the two are evaluated separately and the conversation-level answer repeats across that interaction's rows.
Recording people has rules attached
Consent and retention requirements for call recording vary by jurisdiction and are stricter than for documents. That is your obligation rather than something the pipeline decides — but per-bucket visibility and access grants let you keep transcripts and outcomes restricted to the people who need them.
How the pipeline handles it
- Buckets — Completed recordings export to a bucket automatically, so calls accumulate into something you can query and chart.
- Webhooks — Pushes the output table to your own systems as soon as a recording finishes processing.
- User Roles — Conversation data is sensitive; roles and per-bucket grants keep it to the people running the programme.
Common questions
- What audio formats are supported?
- MP3, WAV and FLAC recordings can be uploaded and processed.
- Can it handle a recording with many separate conversations?
- Yes. A recording is split into its distinct interactions first, and each is classified and evaluated separately — so a full shift at a counter produces per-conversation results rather than one blended average.
- Can I score every call rather than a sample?
- Yes. Rules are evaluated on every turn of every processed recording, which is the practical difference from manual QA — coverage rather than a sample.
Other document types
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