The goal of Innoid is to create a centralized, HIPAA-compliant healthcare data warehouse that unifies clinical and operational data from multiple systems. The platform standardizes structured, semi-structured, and unstructured inputs into a single repository, enabling advanced analytics, business intelligence, and AI-powered insights. It directly tackles the challenge of disconnected healthcare data sources, giving organizations the ability to make faster, evidence-based decisions.

This project delivers an integrated AI and analytics platform for the Foundation for Sickle Cell Disease Research (FSCDR). It connects ECW (eClinicalWorks) EHR data with advanced AI-based predictive models to identify high-risk patients, automate follow-ups, and streamline state grant reporting (CMO99, CMOAE Tasks 5–13).
The goal is to transform static EHR data into actionable insights — improving research visibility, compliance, and patient outcomes while cutting down manual reporting workloads. The solution supports predictive ER-risk modeling, AI-based outreach automation, and Power BI-driven compliance dashboards across FSCDR’s Centers of Excellence.

The objective of the UglyTruck AI Deal Automation Platform was to streamline and scale the truck buying and resale process by introducing an AI powered automation layer over existing QuickBase workflows.
The platform was designed to:

• Automatically identify unsold truck opportunities from historical deal data
• Re engage sellers via email and SMS without manual follow ups
• Interpret seller responses using NLP to assess availability, intent, and image quality
• Provide AI assisted pricing recommendations using proprietary ScrapGo Index data
• Generate marketing assets and facilitate buyer bidding workflows
• Reduce deal cycle time while keeping human control over negotiations

The solution enables semi automated deal execution with strong human in the loop governance, improving efficiency without compromising pricing or compliance.