How itel Went from Manual QA to AI-Powered Quality at Scale

When itel approached us, they had a clear vision but no path to production. Their internal team had experimented with transcription and sentiment analysis, and the results were promising. But they couldn't bridge the gap from a working prototype to a system that operated securely across live operations processing thousands of calls per day.

We embedded with their CTO and Digital Services team for 12 months, building itelligence® from the ground up — not as a vendor delivering a product, but as an extension of their engineering team. This meant understanding their operational constraints, their security requirements, and the specific ways QA managers needed to interact with the data.

The technical architecture uses AWS Bedrock for language understanding, Glue for data pipelines, and QuickSight for the analytics layer. But the real work was in the details: building governance controls that satisfied their security team, designing workflows that fit into existing processes, and creating feedback loops that let the system improve over time.

The results speak for themselves: QA coverage expanded from pilot-level sampling to near-full coverage, manual QA effort dropped by 30-40%, and the client expanded their engagement from $12K to $30K per month based purely on demonstrated value. itelligence® now processes more calls in a day than their manual team could review in a month.