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.
Rol: Qualification workflow and production engineering
Founder contribution delivered as part of professional work at BlandLabs.
- Reported operational saving per year
- $1.2MReported operational saving per yearFounder contribution at BlandLabs
- Eligibility decisioning before callers reach licensed humans
- Real timeEligibility decisioning before callers reach licensed humansFounder track record
- Operational reporting and production monitoring
- Every callOperational reporting and production monitoringFounder track record
El sistema
Qué se construyó realmente
- 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
Experiencia de producto
Del sistema entregado
Tecnología
Stack verificado
Solo se listan las tecnologías confirmadas en el código fuente, la documentación del proyecto o el despliegue en vivo.
- Voice AI qualification workflow
- Real-time intent detection
- Lead scoring and analytics pipeline
Entrega
Alcance de la responsabilidad
- Qualification and decision path design
- Reporting and monitoring
Evidencia
Fuentes
Construya un sistema con una complejidad similar
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