QA coverage from 5% to 100% of calls in 9 months.
A fast-growing MSP was scaling from 30 to 80 agents while its manual QA process was already failing. KeyDelta built an AI scoring engine that reviews 100% of calls, identifies coaching patterns in real time, and turns quality data into agent development. The engine held because we fixed the operating layer first: who owns quality, the coaching cadence that acts on the scores, and a review rhythm that changes agent behavior instead of filling dashboards.
Situation
A fast-growing regional managed services provider was scaling its call center from 30 to 80 agents in under a year. Their manual QA process, supervisors listening to recorded calls and scoring them on spreadsheets, was already failing at 30 agents. They could only review 5% of calls, issues surfaced weeks late through customer complaints, and coaching was reactive instead of proactive.
- Supervisors manually reviewing only 5% of calls, 95% of quality issues went undetected
- QA scores delivered 2-3 weeks after the call, too late to correct behavior in real time
- No trend analysis, recurring issues across agents, shifts, or topics were invisible
- Scaling from 30 to 80 agents meant QA would break entirely without automation
- Agent coaching was gut-feel, not data-driven, supervisors couldn't identify systemic skill gaps
Approach
KeyDelta built an AI-powered QA engine that transcribed, scored, and analyzed every single call, then turned that data into targeted agent coaching.
- 01
Transcription pipeline
Integrated Dubber call recording with OpenAI transcription models for high-accuracy speech-to-text across accents, technical jargon, and crosstalk. Every call transcribed within minutes of completion.
- 02
Multi-model quality scoring
Built a scoring engine using multiple OpenAI models to evaluate calls across six dimensions: greeting, issue identification, technical accuracy, resolution quality, compliance, and closing. Calibrated against 200+ human-scored calls to achieve 89% inter-rater agreement.
- 03
Trend analysis and pattern detection
Aggregated scores across agents, teams, shifts, and issue types. AI identified systemic patterns, specific product areas where agents consistently struggled, time-of-day performance drops, and coaching opportunities invisible in sample-based reviews.
- 04
Coaching feedback loop
QA data automatically generated personalized coaching recommendations per agent. Supervisors received weekly reports highlighting each agent's top improvement area with specific call examples. Training programs updated based on aggregate trend data.
Stack
Outcome
Over nine months, call coverage went from 5% to 100% and the average QA score from 62 to 83 out of 100, while the escalation rate fell from 28% to 13%. QA cost per call dropped from $14 to $0.42, customer CSAT rose from 3.4 to 4.2, and agent retention climbed from 72% to 85%. Builds like this now ship under the KeyDelta Method and stay current under AAMP.
| Measure | Before | After | Change |
|---|---|---|---|
| Call coverage. Every call scored | 5% | 100% | 20x |
| Avg QA score. Targeted coaching | 62 | 83 / 100 | +21 pts |
| Escalation rate. Better first-call resolution | 28% | 13% | −54% |
| Customer CSAT. Better agents, happier callers | 3.4 | 4.2 / 5 | +24% |
| QA cost per call. Scale without headcount | $14 | $0.42 | −97% |
| Agent retention. Data-driven development | 72% | 85% | +13 pts |
“We went from guessing about call quality to knowing, on every single call, every single day. Customer CSAT jumped from 3.4 to 4.2 because our agents got better. Agent retention climbed to 85% because people finally had a development path backed by data, not opinion. QA costs dropped 97% per call. We scaled from 30 to 80 agents without adding a single QA headcount.”