VPO / BPO / Contact Centers

AI Contact Center Quality Assurance & Sentiment Analytics

Analyze contact-center conversations at scale, score interactions against configurable scorecards and identify compliance, sentiment and coaching opportunities.

Speech-to-Text LLM NLP Sentiment Analysis Quality Scoring
Home / AI/ML Solutions / AI Contact Center Quality Assurance & Sentiment Analytics
THE CHALLENGE

Business Challenge

  • Manual QA reviewed only a fraction of calls
  • Scoring varied by reviewer
  • Compliance issues could remain hidden
  • Supervisors needed actionable coaching insights
THE SOLUTION

AI/ML Implementation

  • Transcribe and structure calls
  • AI scorecards for greeting, verification, accuracy, empathy, compliance and resolution
  • Sentiment timeline and escalation flags
  • Identify missed steps and risky statements
  • Generate coaching recommendations

End-to-End Architecture

1
Call Recording
2
Speech-to-Text
3
Conversation Structuring
4
AI Analysis
5
QA + Sentiment + Compliance
6
Supervisor
7
Coaching

Core Capabilities

AI scorecards
Sentiment timeline
Compliance flags
Agent benchmarking
Coaching recommendations
Searchable transcripts
Supervisor alerts

Technology Stack

  • Frontend: React / Next.js + TypeScript
  • Backend: Node.js/NestJS or Python FastAPI
  • Data: PostgreSQL + Redis + object storage
  • ML: Python + scikit-learn/XGBoost/PyTorch as appropriate
  • GenAI: current OpenAI/Gemini models behind a provider abstraction
  • Search: pgvector/OpenSearch or managed vector database
  • Deployment: Docker + managed cloud/Kubernetes where required
  • Observability: OpenTelemetry + centralized logs/metrics

Key KPIs & Success Metrics

  • QA coverage
  • QA score
  • Compliance rate
  • Critical error rate
  • AHT
  • FCR
  • Sentiment improvement

Implementation Roadmap

  • Phase 1: Scorecard definition
  • Phase 2: Transcription pipeline
  • Phase 3: AI evaluation
  • Phase 4: Human QA calibration
  • Phase 5: Dashboard
  • Phase 6: Alerts
  • Phase 7: Governance

AI/ML Lifecycle

  • Data quality and preparation
  • Feature/prompt/retrieval engineering
  • Training or configuration
  • Offline evaluation
  • Human validation
  • Controlled deployment
  • Production monitoring
  • Feedback-driven improvement

Security & Governance

  • RBAC and tenant isolation
  • Encryption in transit and at rest
  • PII protection/minimization
  • Audit logging
  • Model/prompt/version control
  • Human-in-the-loop for low-confidence or high-risk decisions
  • Monitoring for model quality, drift, latency and cost

Case Study Structure

  • Client / Industry
  • Business Challenge
  • AI/ML Solution
  • Architecture
  • Technology Stack
  • Implementation
  • Security & Governance
  • Measured Business Outcomes
  • Future Roadmap

Publication Note: Use verified client names, project screenshots and measured before/after metrics only where contractual and factual approval exists. Otherwise present this as a solution capability or anonymized case study.

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