Every call, understood.
Not a 2% sample.
Your QA team listens to a fraction of calls and decides on that fraction. We transcribe every call, separate who said what, and score each one against your protocol — with the quote that backs every result.
The same recordings answer two completely different questions.
Did the agent follow the protocol?
Every variable of your rubric, evaluated call by call, with the verbatim quote that supports each result and a confidence level. The unit is the call and the subject is the agent — so a sample is enough to rate people reliably.
- Every value traceable to the second it was said
- The reviewer reads the quote instead of re-listening to the call
- Your rubric, your scoring formula, your exclusion criteria
What are your customers actually saying?
The reason for the call is operational and dull — three categories eat 70% of the volume and never change. What matters is what the customer said in passing: they called about a bill and mentioned the new product. That comment is in no survey, no ticket and no review, because that person never filed a complaint.
- Needs the census, not a sample — the value is in the tail
- Analyses only what the customer said, not the agent's script
- Ranked by what changed, not by what is biggest
Why the census: take a theme present in 0.5% of calls. Over 39,000 monthly calls that is about 195 mentions. In a 2.5% sample it is 5 calls — indistinguishable from noise. (Illustrative figures.)
From the recording to a row you can query.
Each call becomes a structured, auditable record. One row per call, one column per variable, plus the evidence and metadata columns.
- 01
Ingest
The audio arrives from wherever you keep it — shared folder, storage bucket or API — and is registered with its metadata: date, time, agent id, case id.
- 02
Transcription and speaker separation
Transcribed in Latin American Spanish. Two-channel audio attributes each channel exactly; single-channel audio is diarised, with structural detection and retry when the diariser collapses two speakers into one.
- 03
Role resolution
Each turn is labelled agent or customer from its content, turn by turn rather than once for the whole call. An isolated separation error cannot contaminate the whole evaluation.
- 04
Variable extraction with evidence
Every variable of your rubric is evaluated against the role-labelled transcript, returning the value, the verbatim quote that supports it, and a confidence level.
- 05
Acoustic reading
Tone, pace and energy are read per speaker from the signal itself and combined with what the person actually said — not from the audio alone, which cannot tell enthusiasm from anger.
- 06
Validation queue
Risky calls are routed to human review. In parallel, a random sample is drawn to measure accuracy. The two queues are separate on purpose — see below.
- 07
Delivery
The structured base is delivered in the agreed format, with the metadata columns you need to join it against your own systems.
- 08
Audio discard
When processing ends the audio file is deleted, unless you need it kept so a supervisor can listen to the evidence. That is your call, and it is worth deciding before deployment.
Four things a competitor cannot copy by adding a feature.
Every result carries its quote
A supervisor who questions a score reads the sentence that produced it. That is what makes human validation viable at scale — nobody has to re-listen to the call or trust the model's judgement.
"No" and "not determinable" are different values
Collapsing them makes the dashboard report non-compliance when the audio was simply unintelligible. It is a real defect a client caught in a demo, and it is fixed by design.
Roles are resolved turn by turn
Systems that decide who is who once per call let a single separation error corrupt the whole evaluation. Ours cannot — and it is also what lets us analyse only the customer's side.
Priced by audio processed, not by seat
You pay for the minutes you actually run. Seat-based pricing charges for agents whether or not there is volume to review.
Measured on your audio and reported. Not quoted from a brochure.
The process runs two separate queues, and the distinction is not a technicality: mixing them biases the estimate and quietly turns a metric back into a promise.
Random sample
Selection must be random. If the reviewer chooses which calls to look at, the estimate is biased and stops being a measurement.
Confidence-driven queue
Concentrates the likely errors: doubtful transcription, non-determinable variables, poor speaker separation. It improves the deliverable but cannot be used to compute accuracy.
About 400 calls give a ±3% margin at 95% confidence — and that number does not grow with volume. It is the same for 5,000 monthly recordings as for 40,000, which makes a fixed measurement sample cheaper than a percentage and statistically equivalent.
What is kept, and what is not.
Kept
- The transcript
- Evaluated variables and their evidence quotes
- Call metadata
- Reviewer corrections, with attribution and date
Not kept
- The original audio file — processed and deleted
Data residency is configurable. If your regulation requires the information not to leave a country or region, the deployment adjusts to it.
Automatic masking of sensitive data — document, account or card numbers — can be applied to the transcript before it is stored.
We act as data processor; you remain the controller. The processing agreement is signed before the first real file is handled.
The rules that make the output worth trusting.
- Ranked by change, not by volume. A ranking by size returns "billing" every week until you cancel.
- Quotes are chosen because they are representative, not because they are striking — and each one says how many calls it stands for.
- The denominator is always stated. Not "62% of customers dislike it", but "of those who called and mentioned it, 62% mentioned it negatively". People who call have a problem; they are not your customer base.
That methodological honesty is not a limitation we are disclosing. It is the argument — it is what separates this from a language model writing plausible summaries.
Your QA sample rates agents well. It finds themes badly.
Both jobs use the same recordings, so it is reasonable to assume one sample serves both. It does not, and the reason is arithmetic rather than opinion.
Bring us twenty of your recordings.
Calibration runs on your real audio, not on a generic demo. Twenty calls are enough to show you how it behaves on your operation before anyone signs anything.
Request a pilot →