Your QA team reviews 2-3% of calls. They find problems, coach agents, move on. But what about the other 97%? What issues are slipping through? What coaching opportunities are missed? Today, We're announcing AI-Powered Quality Assurance and it changes everything.
What AI-Powered QA Analyzes
Every Single Call Gets Scored On:
- Compliance: Required disclosures, prohibited phrases, regulatory adherence
- Soft skills: Empathy, professionalism, active listening
- Process adherence: Script compliance, proper authentication, procedure following
- Resolution quality: Was the issue actually solved?
- Sentiment trajectory: Did the customer feel better at the end than the beginning?
Key Features
Automatic Issue Detection
AI flags calls that need human review based on your criteria. Compliance violations surface immediately not weeks later in a random sample.
Agent Coaching Recommendations
Instead of generic feedback, agents get specific, data-driven coaching: "You scored lower on empathy statements in billing calls. Here are examples of high-scoring agents handling similar situations."
Trend Analysis
Spot systemic issues before they become crises. "Compliance scores dropped 12% this week across all agents on product X calls. Investigate: new product launch may need script updates."
Calibration Tools
AI learns from your QA team's scoring patterns. When humans disagree with AI scores, the system learns and improves.
"We found a compliance issue that had been happening for 3 months but never surfaced in our random sampling. AI-QA caught it in the first week. That alone justified the investment."
Robert Chen Director of Quality, FinServe Corp
Getting Started
Implementation Steps:
- Define your quality scorecard (we provide templates)
- Upload 100 scored calls for AI calibration
- Run parallel scoring (AI + human) for 2 weeks
- Calibrate AI based on discrepancies
- Go live with full monitoring
See AI-QA in Action
Upload a sample call and see the analysis in real-time.
Try AI Quality Analysis →
