Send your recordings and get back timestamped transcripts, every line searchable, with a summary and key quotes that name the agent and the customer. The reviewing is still yours — this is the layer that makes it possible at volume.
timestamps throughout · 99+ languages · your recordings are never used to train a model
A reviewer can listen to maybe six calls an hour, so a queue of two hundred gets sampled — usually the shortest ones, or whichever came in last. The calls that actually matter are the 40-minute ones nobody has time for. Read instead of listen and the sample stops being the constraint.
Upload files, paste a public URL, or submit from wherever your recordings already land — POST /api/v1/transcribe for a file, POST /api/v1/transcribe/url for a link. The Chrome extension records the audio of a call playing in a browser tab, or your own mic, when there's no file to hand.
Speakers are identified and named in the summary, key quotes and chapters; every line of the transcript carries its timestamp.
A summary and chapters turn a 40-minute call into something you can triage in a minute — then jump to the part that decides whether it needs a full review.
Type a phrase and get every line in the call that contains it, each with its timestamp. Competitor names, "refund", "cancel", a policy phrase your team is supposed to use — find it instead of hoping you hear it.
“Where in this call did we promise a refund?”Ask a question about the call and the answer cites timestamps you can click — the transcript jumps there and the audio plays from that line, so a reviewer verifies the quote before it goes in a coaching note.
Timestamped lines you can click, and the search term highlighted where it occurs.
Illustrative example. Speakers are identified and named in the summary, key quotes and chapters; every line of the transcript carries its timestamp.
The calls that ended badly are the long ones. Read the transcript, find the minute where it turned, and quote it exactly in the write-up.
Search a call for the disclosure your team is required to read, or for the promise it isn't allowed to make. One search per call beats one listen.
Find every call where a competitor came up and read the objection in the prospect's own words rather than a rep's paraphrase in the CRM.
A coaching note built on a quoted line with a clickable timestamp lands differently from one built on "I felt like you rushed her".
99 languages, and the transcript stays in the language the call was in — so a reviewer who speaks it can read it, and search ignores accents.
Pull each transcript as JSON with per-segment timing and write it into the tool your scores already live in.
This is the transcript, search and summary layer. It does not score calls, rate agents, compute a sentiment number, fill a QA scorecard, or push into a CRM — there is no such feature, and a page that implied otherwise would waste your evaluation. Your rubric, your spreadsheet, your BI tool.
It also doesn't join calls. No bot dials into a meeting and nothing is transcribed live: you send recordings and get transcripts back asynchronously. Transcription is machine-generated and weakest on names, figures and product jargon — listen to the line before you quote it in anything that affects someone's job.
If recordings land in a bucket or a telephony provider's storage, submitting them is one call per recording. Poll, then pull the transcript.
# submit a recording that's already reachable by URL
curl https://whipscribe.com/api/v1/transcribe/url \
-H "X-API-Key: $WHIPSCRIBE_KEY" \
-H "Idempotency-Key: call-88213" \
-H "Content-Type: application/json" \
-d '{"url":"https://recordings.example.com/call-88213.mp3","source":"api"}'
# then poll GET /api/v1/jobs/{job_id} until status is "done", and read
# GET /api/v1/jobs/{job_id}/result?format=json → text, language, segments[]The job returns the transcript with timestamps plus a summary and key quotes that name the agent and the customer. Idempotency-Key keeps a retried submit from creating a second job for the same call. Full reference in the API docs; keys are issued from /keys, and an agent can query transcripts directly through the MCP server.
Priced per minute of audio, so the bill tracks the queue rather than the headcount. Credits don't expire.
| Plan | Price | Minutes | Roughly |
|---|---|---|---|
| Starter | $8 | 1,000 | 16 hours — a spot-check habit |
| Pro | $12 | 2,000 | 33 hours — one reviewer's month |
| Team | $24 | 5,000 | 83 hours — a small support floor |
Worked example: 25 calls a day averaging 8 minutes is 200 minutes a day, 1,000 minutes across a five-day week, about 4,000 minutes a month — inside Team's 5,000 minutes at $24. A queue half that size fits Pro's 2,000 minutes at $12. Full pricing →
Take the longest recording in the queue, put it through, and see whether reading it beats listening to it. That's the whole evaluation.
Transcribe a call →No — no scorecard, no sentiment score, no agent-performance dashboard. It produces the timestamped, searchable transcript those workflows read from. If someone tells you a transcription engine is measuring empathy, ask to see the rubric.
No. No bot dials in, nothing is transcribed while it's happening. You send recordings — upload, URL, or API — and get transcripts back asynchronously.
WhipScribe names the speakers in the summary, key quotes and chapters; every line carries its timestamp, so you can find each exchange and check it against the audio.
No packaged app. There is a REST API, so writing transcripts back into your own system is a short integration rather than a purchase. See /docs.
They're transcribed on our own servers — not handed to a third-party transcription vendor, and never used to train a model. Audio is deleted after the retention window for your plan; the transcript stays until you delete it. Details on /security.
Up to 10 hours or 5 GB per file, which is longer than any call anyone wants to review. Timestamps run the whole way through.
Before you pay anyone to transcribe: see our transcription service with prices and turnaround.