AI Call Quality & Customer Recovery System

Context

An e-commerce furniture company recorded all customer support calls but lacked a reliable system to evaluate their quality.

Only a small sample of calls was manually reviewed, meaning most conversations were never assessed. As a result, poor customer experiences often went unnoticed until they escalated into complaints.

The business needed a scalable system to analyze 100% of calls, detect low-quality interactions, and trigger timely customer recovery actions.

Problem

The existing QA process was fragmented and inconsistent:

• Only ~10–15% of calls were manually reviewed
• QA scoring varied between reviewers
• Poor customer interactions were often detected too late
• No standardized coaching or feedback loop existed
• Recovery actions depended on manual detection
• QA data was not centralized or actionable in real time

Solution

A fully automated AI-powered QA and customer recovery system was implemented to evaluate every call, generate structured feedback, and trigger escalation workflows for poor experiences.

Each call is automatically transcribed, scored against a structured QA rubric, and stored in a centralized data layer. Calls below a defined quality threshold are immediately escalated for human review and recovery action.

Managers receive AI-drafted recovery messages via Telegram and can approve or reject them with one click, ensuring fast and controlled customer recovery.