SAEPUL MALIKAI Call-Center Agent Copilot
Real-time transcription and a live checklist that guide call-center agents mid-call, then auto-score every session against a rubric.
Problem
Prior QA only sampled a fraction of agent–customer calls for manual review — roughly 10 out of every 100 sessions across all ~11 agents — leaving most calls unchecked. The product team wanted agents guided in the moment, not just graded after the fact.
Architecture
Live calls are transcribed with OpenAI's real-time transcription model. Per-case knowledge — what should be asked or checked for a given call type — is stored in a data table and pulled in by the AI to drive both the live checklist and the post-call evaluation, rather than relying on one generic prompt for every call type.
Execution
Built jointly with 2 people from the product team, whose idea it was. Two hard problems stood out: transcription accuracy, particularly separating the agent's voice from the customer's amid background noise, and getting the AI's rubric-based scoring to land close enough to a human QA reviewer — some subjective rubric dimensions still carry a scoring margin of error above 5.
Impact
Moved QA coverage from a ~10% manual sample to 100% of sessions scored automatically, across all ~11 agents. Still testing-scale as a proof of concept, not yet rolled out product-wide.