Insurance claim intake is one of the most interesting voice AI use cases because it combines urgency, structured data extraction, and empathy in a single conversation.
Here's how we approached it:
The Conversation Flow
A claim call has a predictable structure but unpredictable content:
- Caller is often stressed/upset
- Need to extract: policy number, incident type, date, location, severity
- Need to classify urgency (emergency vs. routine)
- Need to either triage to a human or complete intake autonomously
Key Technical Decisions
Sentiment-aware responses: The AI adjusts tone when it detects distress. A car accident caller gets a different conversational style than someone reporting a minor property claim.
Structured extraction with fallbacks: We use a schema-driven approach — the AI knows exactly what fields are needed and will naturally work them into conversation rather than running through a checklist.
Urgency routing: Real emergencies (someone injured, active flooding, etc.) get immediately transferred to the broker's mobile. The AI doesn't try to handle these autonomously.
What Surprised Us
The biggest challenge wasn't technical — it was caller trust. Insurance callers need to feel heard. We found that having the AI briefly summarise what it understood ("So you had a rear-end collision on the N7 yesterday afternoon, and you'd like to start a claim — is that right?") dramatically improved completion rates.
Anyone else working on voice AI for professional services? The empathy layer is genuinely hard.
