AI IVR Insurance Agent System
↓ 70% call handling time
Industry
Insurance
Service
AI Voice Agents
Client
Insurance Company (confidential)
Timeline
10 weeks
The Problem
[TODO: Describe the client's situation — what was their call centre handling? What was the volume? What was the manual cost?]
[TODO: What specific pain points did they have — wait times, handling time, agent burnout, after-hours coverage?]
[TODO: Why did they approach Aryan? What had they tried before?]
The Approach
Call flow audit
[TODO: Describe what you found when you mapped their current call flows. What were the most common call types? What data did agents need?]
Architecture design
[TODO: Describe the technical architecture — Twilio for call handling, Deepgram for STT, OpenAI for reasoning, TTS for responses. How was session state managed?]
Build and test
[TODO: How did the build proceed? What were the hardest technical challenges — latency, accent handling, edge cases?]
Production deployment
[TODO: How was it deployed? What does the monitoring setup look like? What happened in the first week of live calls?]
The Results
Average call handling time
Coverage (was 8 hours)
First-call resolution rate
Cost per handled call
Key Takeaways
- →[TODO: Key insight 1 — e.g., 'Real-time audio streaming latency is the hardest engineering problem in voice AI — every design decision flows from it.']
- →[TODO: Key insight 2]
- →[TODO: Key insight 3]
- →[TODO: Key insight 4]
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