Customer Self Service

Customer queries and knowledge base data power conversational AI and agentic AI to reduce handling time and increase self-service

Business impact

• Customer handling time — Self-service reduces time customers spend waiting for agent assistance by 40%
• Operational efficiency — Automated workflows and AI agents decrease manual workload and case volume
• Customer satisfaction — Faster, personalized responses increase satisfaction and loyalty
• ROI per voice session — Conversational AI generates measurable financial returns per interaction
• Agent productivity — Agents focus on complex cases as AI handles routine queries

Data requirements

• Customer interaction logs (Text) — Used to train AI on common queries and intents
• Knowledge base articles (Text) — Provide grounding data for AI to deliver accurate answers
• Voice recordings and transcripts (Audio) — Enable conversational AI to understand and respond to voice queries
• Customer profile and transaction data (Structured) — Support personalized responses in authenticated portals
• Search query data (Text) — Optimize content tagging and AI response relevance

AI methods and techniques

• Agentic AI — Coordinates multiple AI agents to handle intents and escalate complex cases
• Conversational AI — Processes natural language queries for voice and text self-service
• Predictive AI — Anticipates customer needs and suggests relevant content or actions

AI models and model families

GPT-4o, Claude, Gemini 1.5 Flash, Custom LSTM and Transformer models

Industries

Real-world evidence

3 documented case studies on record.

Companies using this: CDL, Kore.ai, Oro Commerce.