Maya

Maya is a voice-first AI telephony agent designed to automate hospital reception and call handling workflows. It manages inbound and outbound patient calls, answers enquiries, books appointments, routes calls to appropriate departments, and reduces manual reception workload. The system supports multilingual conversations and can be configured based on hospital workflows, operational policies, and service offerings. This product represents a shift from manual reception operations to scalable, AI-driven hospital communication infrastructure. This product expands the platform from traditional software workflows into AI-driven operational automation.

Hospitals face operational inefficiencies due to manual call handling:

• High reception workload managing appointment booking and enquiries
• Missed calls and delayed responses during peak hours
• Inconsistent patient experience due to human limitations
• Limited scalability without increasing staffing costs
• Lack of structured automation for routine call workflows

These constraints reduce operational efficiency and create scalability bottlenecks.

I contributed to Maya’s product definition, deployment readiness, and commercial structuring while parallelly owning other core product systems. My key contributions included:

• Collected structured requirements from hospital stakeholders and partners
• Defined Maya’s pricing model aligned with hospital usage and value delivered
• Contributed to product roadmap planning and capability prioritization
• Tested voice interaction flows, workflows, and deployment scenarios
• Identified operational edge cases and provided product feedback
• Supported product positioning, scope clarity, and deployment readiness
• Validated Maya’s behavior through structured testing across real hospital usage scenarios to ensure reliable workflow execution

My work focused on ensuring Maya was operationally deployable in real hospital environments, aligned with workflow constraints, and structured for scalable commercial rollout.

Maya enables hospitals to automate key reception workflows:

• Handles inbound patient calls automatically
• Books appointments based on hospital schedules
• Routes calls to appropriate departments when needed
• Supports outbound calls for reminders and operational communication
• Responds to patient enquiries instantly
• Supports multilingual voice interactions
• Configurable based on hospital workflows and policies

These capabilities reduce manual workload while improving responsiveness and operational efficiency.

I contributed to translating real hospital workflows into deployable AI product requirements. Key product definition contributions included:

• Identified real-world hospital call scenarios and operational workflows
• Defined deployment requirements for hospital integration and usage
• Tested AI voice interaction workflows across multiple use cases
• Identified failure cases, edge scenarios, and operational constraints
• Helped refine capability scope based on feasibility and hospital needs

This work helped ensure Maya could function reliably in production hospital environments.

I contributed to defining Maya’s commercial and product rollout strategy:

• Designed pricing structure aligned with hospital usage patterns
• Defined monetization logic for scalable deployment
• Structured roadmap inputs based on operational priorities
• Helped align product scope with partner and business requirements
• Supported positioning Maya as an operational automation product for hospitals

This ensured Maya was both technically viable and commercially deployable.

Maya can be deployed across hospital operations to automate:

• Appointment booking and rescheduling
• Reception enquiry handling
• Department routing and escalation
• Patient reminders and operational notifications
• After-hours call handling

This enables hospitals to operate reception services without increasing staffing overhead.

Maya represents a foundational step toward AI-driven automation in hospital operations. Key strategic impact:

• Enables automation of manual reception workflows
• Reduces operational load on hospital staff
• Improves responsiveness and patient experience
• Establishes AI as a scalable infrastructure layer in healthcare operations
• Opens a new AI product category beyond traditional healthcare apps

My contributions helped translate Maya from a technical capability into a deployable product by ensuring it was operationally viable, commercially structured, and aligned with real hospital workflows.