Resolution Recommendation

Customer call transcripts and metadata predict call reasons and recommend resolutions with high accuracy.

Business impact

• Customer Satisfaction — Higher satisfaction results from faster, more accurate issue resolution and personalized support
• Operational Costs — Reduced costs due to decreased call handling time and improved agent productivity
• Case Resolution Time — Shorter resolution times as AI recommends relevant solutions based on call analysis
• Agent Efficiency — Agents handle more cases effectively with AI assistance in diagnosis and recommendations

Data requirements

• Customer call transcripts (Text) — Used to analyze call reasons and extract intent for resolution recommendations
• Call metadata (duration, time, agent ID) (Structured) — Provides structured context to improve prediction accuracy and agent performance tracking
• Customer interaction history (Structured) — Enables personalized recommendations based on past issues and resolutions

AI methods and techniques

• Predictive AI — Predicts call reasons and likely resolutions from historical call data patterns
• Generative AI — Generates personalized response suggestions and communication templates for agents
• Agentic AI — Supports autonomous recommendation workflows and case swarming coordination

AI models and model families

GPT-4o, Claude, OpenAI GPT-4, Anthropic Claude

Industries

Real-world evidence

1 documented case study on record.

Companies using this: Verizon Communications.