An AI-powered Quality Assurance (QA) tool developed to enhance the customer support experience by automatically analysing customer-agent conversations( both actual human agents as well as agentic bots too). It identifies policy violations, customer sentiment, and agent performance insights in real-time. Platform integrates with CRMs and uses machine learning (ML) and natural language processing (NLP) to offer actionable insights for businesses seeking to improve service quality and operational efficiency. This will help organisations to streamline support analysis, improve agent training, and monitor customer satisfaction metrics. 

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 proposed application is to enhance the management of chronic care disease by facilitating comprehensive data collection, treatment adherence, and patient-provider communication. The application aims to address the current lack of real-world data and studies in urology. It enables healthcare providers to monitor patient-reported symptoms, track pain trends, and assess treatment efficac Doctors can access a dashboard displaying all  patients, prioritize cases based on symptom severity, and receive alerts for critical cases. Patients can easily log symptoms, monitor their  scores, and receive reminders for medications and lifestyle changes, ensuring better adherence to treatment plans. It enables secure messaging between patients and providers, allowing for timely updates, treatment adjustments, and symptom reports. Patients can access personalized educational materials, including multimedia resources and chatbot support, to manage their condition effectively. The platform aims to deliver personalized, data-driven care, improve patient outcomes, and contribute valuable real-world evidence to further research and understanding of the problem