Key Business Impact

Reduced call-handling cost from ~$15 to <$0.01 per call; 97% cost reduction with 70-80% automation rate.

Project Overview

Voice-activated scheduling system automating healthcare calls with agentic flows, replacing manual phone operations.

Technical System Architecture

Operational data flow and system architecture designed for this solution:

input

Patient Call

ai

Voice AI Agent

process

Model Context Processing

database

Scheduling API

output

Call Confirmation

Case Study & Delivery

Designed and delivered voice automation for surgical scheduling across inbound/outbound calls, integrating EMR connectivity and compliance workflows.

Consulting Assessment & Strategy

As an AI consultant, the primary focus for this project was to establish a production-grade infrastructure that balances LLM performance, response latency, and system cost. This was achieved by introducing specific design patterns:

  • Agentic Orchestration: Decoupling tasks into dedicated specialized agents to reduce complexity and improve reasoning accuracy.
  • Custom Model Routing: Routing simple tasks to lightweight tier-2 models (e.g. AWS Nova Flash / Sonic) and reserving heavy reasoning for flagship models.
  • Security & Compliance Guardrails: Integrating strict input/output verification steps to prevent PII exposure and prompt injections.