Call Center QA System
WHY I BUILT IT
Listening to sales calls doesn't scale.
If you want to improve performance, someone has to sit there, listen to recordings, take notes, and figure out what went wrong.
It's slow, inconsistent, and most of the time it just doesn't happen.
So calls keep happening, mistakes keep repeating, and nobody really knows where things are breaking down.
I didn't want to rely on manual review.
I wanted the system to handle it.
WHAT I BUILT
An AI-driven call analysis system built directly into our outbound infrastructure.
Every call is:
Recorded automatically
Analyzed by AI
Broken down for quality, performance, and gaps
Then a second layer processes that into a clean report.
It looks at:
How the conversation was handled
What was said vs what should've been said
Where the call dropped off
What could be improved
All without anyone having to listen to the call.
WHAT HAPPENED
It removed manual QA completely.
Instead of reviewing random calls or guessing what's wrong, we get consistent feedback across every call.
Patterns show up fast.
You can see exactly where performance is breaking down and fix it without digging through hours of recordings.
WHAT DIDN'T WORK
It's not standalone.
It depends on the full system around it. Calls, recordings, prompts, and context all need to be in place.
It also requires tuning.
Out of the box, AI gets close, but not perfect. It needs adjustments to stay accurate and actually useful.
CURRENT STATE
Fully integrated into the internal outbound system.
Actively used for call quality and performance tracking.
Still being refined, but already replacing manual review entirely.