AI-assisted Medicare qualification with structured human hand-off
Reported operational saving: approximately $1.2M per year.
An AI-assisted Medicare and ZIP-code qualification workflow with real-time decisioning, structured hand-off and operational reporting, credited with a reported saving of about $1.2M per year.
US Medicare advisory operations. Callers are qualified on eligibility and geography before reaching licensed humans, so agent time is spent only on qualified conversations.
Role: Qualification workflow and production engineering
Founder contribution delivered as part of professional work at BlandLabs.
The system
What was actually built
- Medicare and ZIP-code eligibility qualification
- Agent decision path with escalation to humans
- Lead scoring and structured hand-off
- Operational reporting on every call
- Production monitoring
Technology
Verified stack
Only technologies confirmed in source code, project documentation or the live deployment are listed.
- Voice AI qualification workflow
- Real-time intent detection
- Lead scoring and analytics pipeline
Delivery
Scope of responsibility
- Qualification and decision path design
- Reporting and monitoring
Evidence
Sources
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