Support call QA automation

A QA scorecard, mechanically

A typical scorecard has 15-30 questions of three types: presence questions ("Did the agent greet using customer's name?"), tone questions ("Was the agent's tone empathetic?"), and adherence questions ("Did the agent follow the troubleshooting flow?"). Each is scored Yes/No or 0-3. CallScribe automates the presence questions directly (transcript regex + NER), the tone questions via dialect-aware sentiment models, and the adherence questions via a sequence-matching approach against your defined troubleshooting flow.

No scorecard automation is 100% reliable. Mature deployments accept that the model is uncertain on 10-20% of calls per question and route those calls to human review explicitly, while the 80-90% confident-correct calls receive automated scores with audit trails.

What the QA analyst now actually does

Pre-CallScribe: rate 50 randomly-sampled calls per week. Post-CallScribe: review the model's low-score and low-confidence calls, calibrate-rate borderline cases, and investigate patterns the system surfaces (an agent whose talk-time is rising, a scorecard question whose model accuracy is degrading). The number of calls the analyst sees is similar; the calls are more diagnostic, and coverage is now 100% instead of 5%.

This shifts QA from a sampling exercise to a population-monitoring exercise. The questions QA can answer change: "what fraction of calls in May exhibited improper greeting?" becomes a real number instead of an extrapolation from a small sample.

Edge-case detection: where humans are still needed

Some scorecard questions resist automation. "Did the agent demonstrate appropriate empathy for the customer's emotional state?" is the canonical example — a model can score sentiment, but matching agent behaviour to customer emotional context is a judgment call. CallScribe flags such calls (high customer negative sentiment, ambiguous agent response) for human review specifically, so QA analysts spend their time exactly where the model can't replace them.

Calibration against existing QA programs

A new QA-automation deployment needs calibration against existing human ratings to be trusted. We run a calibration study during onboarding: human raters score the same 100-200 calls the model scored; we measure agreement per scorecard question; questions with weak agreement either get tuned (custom rules, training-data additions) or get marked as "human-only" in the scorecard. The output is a deployment that QA leadership trusts because they've seen the calibration data, not because we promised accuracy.

Reporting and rep-facing dashboards

QA scores aggregate to per-rep, per-team, per-disposition, per-time-period views. Reps see their own scores in their dashboard with example calls (top-3, bottom-3) per scorecard question. Team leads see distribution, outliers, and time trends. Operations directors see scorecard performance vs. CSAT vs. AHT — the cross-tabulation reveals whether scorecard adherence actually drives outcomes (often yes for some questions, no for others, and the no-correlation questions deserve scorecard re-design).

What QA automation does not replace

It does not replace listen-along coaching where a senior takes a difficult escalation alongside a new agent. It does not replace structured 1:1 coaching sessions where a coach uses two or three calls as discussion fodder. It does, however, dramatically improve the inputs to those activities — coaches arrive with the right calls already identified, not chosen at random.