Astra Health is an AI-driven platform designed to revolutionize clinical documentation. By leveraging multilingual AI-powered solutions, it enables healthcare professionals to automate their administrative tasks, allowing them to focus on providing quality patient care. The platform enhances clinical productivity, ensures accurate documentation, and improves the overall patient experience while reducing administrative burdens. Astra Health aligns with the mission of making healthcare more efficient and patient-centered. It supports diverse healthcare professionals across different languages and settings, ensuring seamless integration into existing medical workflows.

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. 

The primary objective of this project is to design and implement an event-driven backend system that automates the generation, validation, and delivery of healthcare-related EDI (Electronic Data Interchange) files, such as 834 (Eligibility and Enrollment), 837 (Claims), and 835 (Remittance Advice). These files are essential for secure, standardized, and compliant data exchange between healthcare providers, payers, and regulatory partners.

This system aims to:

• Ensure compliance with HIPAA and EDI X12 standards through rigorous validation (via Cotiviti).
• Enable seamless integration with external vendors like Avesis, Nations Benefits, and Reveleer through SFTP-based delivery mechanisms.
• Maintain operational transparency and traceability using real-time monitoring and logging via SigNoz.
• Support future scalability and workflow orchestration through modular design and integration with AWS services like SQS and Step Functions.