AI has unlocked unprecedented innovation and efficiency in healthcare, but it’s also facing increasing regulatory friction.

After spending years building AI systems for Healthtech as an ML Engineer, I saw the problem up close: weeks lost waiting for data access approvals, endless work on redaction and tagging pipelines, constant trade-offs between AI performance, personalization, and privacy, and confusion of what’s “too sensitive”.

Healthtech innovation is no less than walking on eggshells, with teams burning resources and time to stay compliant.

That’s why we’re building EgiSphere, a plug-and-play privacy gateway for AI-ready data.

It acts as a universal privacy-aware data layer sitting between your data sources and AI workflows, intelligently classifying, mapping, transforming, and governing sensitive data so teams can query, analyze, and train AI with zero exposure.

EgiSphere combines advanced privacy-preserving algorithms like differential privacy, smart aggregation, anonymization, and real-time synthetic data generation with a self-learning compliance copilot that continuously adapts to changing regulations and data risks.

Our mission is clear: privacy and trust should never be a friction to progress, especially when the mission is lifesaving.

We’re here to make sure researchers, data scientists, and engineers can move faster, safer, and smarter, delivering better care, faster, with compliance built in from day one.