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AI call center quality assurance with 100% coverage.

Most QA teams hear 1 to 3 percent of calls and guess the rest. We deploy AI review across every recorded call: per-criterion compliance verdicts backed by quoted evidence, a coaching tip per call, and structured data — all landing in a dashboard your QA team can actually query.

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- QA that samples 1 to 3 percent of calls and hopes

- scores without evidence, so every review becomes a dispute

- compliance drift discovered months late, in an audit

+ every call scored against your rubric: pass, fail, or n/a per criterion

+ each verdict backed by a quoted line from the call itself

+ an overall compliance percentage and a coaching tip per call

how it works

From recording to receipts

  1. 01

    Ingest

    Recordings flow in continuously from your existing telephony or storage. No workflow change for agents.

  2. 02

    Transcribe & separate

    Every call becomes a speaker-separated transcript, ready for scoring.

  3. 03

    Score against your rubric

    A multimodal AI evaluates each criterion from your own QA standards, quoting the exact line behind every verdict.

  4. 04

    Query, coach, prove

    Results land in a filterable dashboard. QA leads move from spot-checking to querying; disputes end at the quoted evidence.

what you get

  • every call scored against your rubric: pass, fail, or n/a per criterion
  • each verdict backed by a quoted line from the call itself
  • an overall compliance percentage and a coaching tip per call
  • structured data extracted from the conversation automatically
  • a dashboard filterable by agent, campaign, criterion, and score

Frequently asked questions

What is AI call center quality assurance?
AI call center QA uses a model to review every recorded call against your compliance rubric, producing per-criterion verdicts, an overall score, and coaching guidance, instead of a human team sampling a small fraction of calls.
Can AI really review 100 percent of calls?
Yes. The pipeline ingests recordings continuously with bounded concurrency and automatic retries, designed around a ceiling of hundreds of calls per minute. Coverage stops being a staffing question and becomes an infrastructure property.
How do we know the AI's scores are right?
Every verdict is backed by a quoted line from the call, so a human can verify any score in seconds. Scoring follows your own rubric rather than generic sentiment, and thresholds are tuned with your QA team during rollout.
Does this replace the QA team?
It refocuses them. Humans stop sampling and start coaching: the AI provides complete coverage and evidence, and your QA leads spend their time on the conversations and agents that need attention.
What does call recording QA include beyond scoring?
Speaker-separated transcripts, per-criterion compliance verdicts with quoted evidence, an overall compliance percentage, a coaching tip per call, and structured fields extracted from the conversation for downstream systems.

Hear what your other 97 percent of calls sound like.

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