Outcome & Business Impact

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

Challenge & Context

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

Production System Architecture

Operational data flow and system architecture delivered by our team (select any node to inspect its role and rationale):

input

Patient Call

ai

Voice AI Agent

process

Model Context Processing

database

Scheduling API

output

Call Confirmation

Interactive Blueprint: Select any component above to review architecture rationale & guardrails
input

Patient Call

Component Purpose

User requests, uploaded data payloads, text strings, or API webhooks triggering the pipeline.

Architectural Rationale

Inputs are validated, sanitized (scrubbing PII), and formatted before reaching downstream models to prevent prompt injections and protect data privacy.

Approach & Delivery

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

How Our Team Delivered

Guided by The AI Trust Stack™, our advisory and engineering team designed this capability for governed, reliable operation in production:

  • Architecture & Data Foundations: Decoupling workflows into well-bounded components with trusted retrieval and integration patterns.
  • Observability & FinOps: Matching model tiers, caching, and deterministic processing to workload complexity for predictable unit economics.
  • Governance & Human Oversight: Enforcing input/output controls, privacy safeguards, and clear human escalation paths.