Automatically evaluates 100% of customer interactions against your quality scorecard—identifying coaching opportunities, compliance risks, and top performer patterns—at a fraction of the cost of manual QA.
"We have 40 agents handling 3,000 conversations a day. Our QA team of 4 can review about 100 conversations. That's 3.3%. We pick them randomly, which means we're essentially relying on luck to find problems. Last quarter, we had a compliance incident—an agent was sharing internal pricing that wasn't public. We only found out when a competitor somehow had our rates. Turns out the agent had done it dozens of times over two months. Never in our random sample. Our QA scores are a joke. We know they're inconsistent. I've seen the same conversation scored 15 points apart by different analysts. But what choice do we have? We can't afford to hire 30 more QA people. And even if we could, they'd all score differently anyway. We need a way to review everything, consistently, in real-time. Not a sample. Everything."
— Director of Quality, Financial Services Contact Center
Deploy an AI agent that reviews every single conversation against your quality scorecard—identifying strengths, improvement areas, compliance issues, and coaching opportunities—with the consistency of a machine and the nuance of an expert.
Every conversation reviewed. Every channel. Every agent. No more random sampling. No more hoping the problems surface. If it happens, you know about it.
Same criteria, every time. No mood variations. No subjective interpretation. Your quality scores become reliable data you can actually use for decisions and trends.
Reviews complete within minutes of conversation end. Coaching opportunities identified immediately. Agents get feedback while the interaction is still fresh.
Evaluates conversations against your custom quality scorecard with detailed scoring.
Detects compliance violations and policy adherence issues automatically.
Deep analysis of conversation quality, tone, and effectiveness.
Identifies specific coaching opportunities with actionable feedback.
Comprehensive metrics and trends for agents, teams, and organization.
Identifies and catalogs excellent interactions for training and recognition.
Financial services company moves from 3% random sampling to 100% automated review. Compliance incidents drop 89%. Coaching effectiveness improves 340%.
"Before: 100 conversations reviewed daily by 4 QA analysts. Random sampling. Average feedback delay: 9 days. Compliance incidents discovered after customer complaint: 12 per quarter. After: 3,000 conversations reviewed daily by QA Agent. 100% coverage. Average feedback delay: 47 minutes. Compliance incidents caught before impact: 156 per quarter (all addressed immediately). Compliance incidents discovered after complaint: 1 per quarter (edge case). Cost comparison: Manual QA team: $240K/year for 3% coverage. QA Agent: $85K/year for 100% coverage. Net savings: $155K/year with 33x more coverage."
Agent promises a customer a discount they're not authorized to give. QA Agent flags it immediately. Manager intervenes before fulfillment creates a precedent.
"🚨 COMPLIANCE FLAG - Unauthorized Promise Detected. Conversation: TKT-78456, Agent: David Kim. Customer request: 'Can you give me a discount since I've been a customer for 5 years?' Agent response: 'Absolutely! I'll apply a 25% loyalty discount to your account right now.' Policy violation: Agents authorized for max 10% discount without manager approval. 25% requires VP approval. Risk: Precedent setting, margin impact, policy inconsistency. Immediate action: Manager Jennifer Walsh notified via Slack. Recommended: Intervene before discount applied. Follow-up: Coaching scheduled for David Kim on discount authorization levels. Pattern note: Third unauthorized promise this month for David Kim."
New agents receive real-time feedback on every conversation. Specific, actionable coaching accelerates skill development. Time to proficiency cut in half.
"New Hire: Alex Rodriguez (Week 2). Conversations today: 24. Average score: 71% (target: 85% by Week 6). Strengths identified: Excellent greeting and identification (98%), Strong empathy and rapport (89%), Good product knowledge (84%). Improvement areas: Solution accuracy (62%) - Providing incomplete or incorrect solutions. Specific issue: Troubleshooting steps missing from 8 of 24 conversations today. Example: TKT-78234 - Customer asked about password reset, Alex provided link but didn't mention 24-hour expiration or spam folder check. Coaching recommendation: Review troubleshooting checklist module. Shadow Sarah Martinez (top performer) for 2 hours. Progress tracking: Day 1: 58% → Day 5: 64% → Day 10: 71%. Projected proficiency: Week 3 at current trajectory (vs. 6-week average)."
QA Agent identifies what top performers do differently. Specific techniques extracted and turned into training. Team average improves 18%.
"Top Performer: Sarah Martinez (94% average, #1 ranked). Conversations analyzed: 847 (last 30 days). Unique patterns identified vs. team average: (1) Acknowledgment before solution: Sarah acknowledges the customer's situation in 94% of conversations before jumping to solutions. Team average: 41%. Impact: 23% higher CSAT on acknowledged conversations. (2) Proactive next steps: Sarah provides 'what happens next' in 89% of resolutions. Team average: 34%. Impact: 31% fewer follow-up contacts. (3) Technical translation: Sarah explains technical concepts with analogies in 67% of technical conversations. Team average: 12%. Impact: 28% higher comprehension scores. Training recommendation: Create module on 'Acknowledgment First' technique with Sarah's examples. Add 'Next Steps' to resolution checklist. Build analogy library from Sarah's conversations. Projected impact: If team adopts these patterns, estimated +18% average quality score."
Configure your exact quality criteria with weighted scoring and auto-fail rules.
Reviews chat, email, phone, and social with channel-appropriate criteria.
Evaluates conversations in 30+ languages with native understanding.
Immediate notification of compliance issues and coaching opportunities.
Compare AI scores to human reviews for continuous calibration.
Agents can dispute scores with manager review and feedback loop.
Identifies emerging quality issues before they become widespread.
Leaderboards, badges, and recognition to drive quality improvement.
Complete record of all evaluations for compliance and regulatory needs.
Reports to: Quality Manager
Availability: 24/7/365
Scope: All customer interactions
Complete specification including scorecard configuration, compliance rules, and feedback templates.
Download .docxConfigure custom scorecards, define compliance rules, and set alert thresholds and coaching triggers.
Pay once. Own the asset. Full source code. Deploy on your infrastructure.
All conversations and quality data never leave your infrastructure.
New evaluation models, scoring improvements, and integration updates.
Configure scorecards, compliance rules, and coaching triggers.
Deploy the Quality Assurance Agent on your infrastructure. Every conversation reviewed. Consistent scoring. Real-time coaching. 100% coverage at a fraction of the cost.
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