Large-scale Health Data Management & AI Enablement

A leading health tech organization engaged our team to architect, design, and build a scalable platform that empowers healthcare organizations to analyze and interrogate their data using Large Language Models (LLMs) trained on both proprietary and third-party data sources.

The platform features a flexible scoring system for data quality, aligned to each client’s custom standards. This includes configurable thresholds for data validation and accuracy, enabling the platform to apply a medallion architecture that ensures interactions occur only with trusted, verified data.

To support these capabilities, we developed a secure, HIPAA-compliant infrastructure capable of processing and storing both PHI and PII. Sensitive data is appropriately masked and obfuscated based on regulatory and industry standards. The platform supports on-demand creation and versioning of LLMs, which can be trained on a variety of shared knowledge bases. Custom chatbots, powered by prompt engineering, allow users to interact with the models in ways tailored to their organizational needs.

On the backend, the platform was designed to be cloud-agnostic, with full compatibility across GCP, AWS, and Azure. After a comprehensive review, the client opted to deploy within the Azure ecosystem, following our strategic recommendation.