Building intelligent healthcare systems where trust, precision, and human impact matter.
01 The Problem
Healthcare is reactive, not predictive. Post-operative care relies on lagging indicators, meaning complications are often caught only after they escalate. In high-stakes environments, the gap between data generation and clinical decision-making can be the difference between recovery and readmission.
02 The Vision
We set out to build Neuralyn not just as an algorithm, but as a safety layer for clinical teams. Our goal was to close the loop—transforming raw physiological signals into real-time, actionable foresight.
Core Principles
- Trust over Black Boxes
- Precision at Scale
- Seamless Clinical Integration
03 The System
Signal Ingestion
High-fidelity data capture from diverse clinical monitoring endpoints.
Predictive Core
Ensemble models detecting subtle deviations before thresholds are breached.
Clinical Delivery
Context-aware alerts that integrate directly into provider workflows.
04 Execution
As COO and Co-Founder, my role extends beyond code. It is about orchestrating the collision of AI strategy, product development, and operational reality.
I lead the execution of our roadmap, ensuring that our technical ambition never outpaces our clinical responsibility. This involves bridging cross-functional teams—translating data science breakthroughs into robust, deployable product features while navigating the complex regulatory landscape of healthtech.
05 Impact
Concept to Deployment
Led the platform from initial research architecture to a production-ready clinical support system.
Focus on Trust
Established XAI (Explainable AI) protocols as a non-negotiable standard for all model outputs.
“Healthcare AI succeeds only when intelligence earns trust.”
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