AI Call Audit
Manual call auditing covered less than 10% of total call volume. Compliance gaps went undetected. Coaching was inconsistent and reactive. We deployed a fully automated AI call audit system processing 100% of call volume — across multiple languages and accents — in one month.
Auditing 10% and Hoping for the Best Is Not a Compliance Strategy
For enterprises running large contact centres — in banking, insurance, fintech, or outsourced BPO — call quality has always been a numbers problem. Manual QA teams can only listen to a fraction of calls. The rest go unreviewed.
The consequences are real: compliance breaches that only surface at audit time, agents who miss coaching because their issues never get sampled, and customer experience signals that disappear into unreviewed recordings.
The client needed a system that could handle their full call volume, work across the accents and languages their agents operated in, and produce structured output that managers and compliance teams could act on — without requiring a large QA headcount to run it.
What We Built
An end-to-end AI call audit pipeline: audio ingestion, multi-accent and multi-language transcription, compliance rule application, sentiment scoring, agent performance classification, and structured reporting — all automated.
Execution
From initial discovery to a live, production-grade system in one month. The deployment followed a three-phase approach: requirements and data access in week one, model tuning and integration in weeks two and three, and live deployment with manager training in week four.
From 10% Coverage to 100%. In One Month.
Beyond the numbers: compliance teams moved from reactive to proactive. Managers had structured coaching triggers rather than anecdotal observations. And the business had a real-time view of customer sentiment that it had never had before.