AI Call Center Quality Assurance With 100% Call Coverage
A multimodal AI platform that scores every call-center recording for compliance, extracts structured data, and coaches agents, replacing sampled QA.
Most call centers assure quality by listening to a sample: typically 1 to 3 percent of calls. Everything else goes unheard. For a high-volume outbound call center, we built a platform that reviews every call instead.
The problem
Human QA does not scale. A team that samples a few calls per agent per week cannot catch systematic compliance drift, cannot coach from evidence, and cannot answer a simple question: what actually happened on the other 97 percent of calls?
What we built
An end-to-end AI review pipeline. Raw call recordings go in; what comes out, for every single call, is:
- a speaker-separated transcript,
- a score for each compliance criterion, marked pass, fail, or not applicable, each backed by a quoted line from the call as evidence,
- an overall compliance percentage,
- a concrete coaching tip for the agent,
- structured data extracted from the conversation itself.
Results land in a filterable dashboard built to span tens of thousands of calls, so QA leads move from spot-checking to querying: by agent, by campaign, by criterion, by score.
How it works
A multimodal model (Google Gemini) evaluates the audio against the center's own compliance rubric, so scoring reflects the client's actual standards rather than generic sentiment. Behind it sits a production task pipeline: scheduled ingestion, bounded concurrency, automatic retry on transient failures, and a self-migrating database schema, so the system absorbs new recordings continuously without an operator babysitting it.
What it proves
Compliance review stops being a sampling exercise and becomes a complete, auditable record. Every score traces back to a quoted moment in a real call, which makes disputes short and coaching specific. This is the pattern we bring to any operation drowning in recorded conversations: turn the archive into structured, queryable evidence.
We deploy this for other call centers, tuned to their rubric. Tell us about yours.
Frequently asked questions
- What is AI call center quality assurance?
- A model reviews 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 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 QA leads spend their time on the conversations and agents that need attention.
- What does it 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.