VigilAI- Agentic AI Platform for Autonomous Pharmacovigilance

It leverages a robust AWS stack- EKS, ECR, API Gateway, Application Load Balancer, Bedrock, Kendra, and DynamoDB to deliver secure, containerized orchestration, intelligent search, LLM-driven reasoning with enterprise-grade compliance.

### Challenges:

Traditional safety operations are built on manual, fragmented, and sequential workflows that take 7–15 days per case and rely heavily on human intervention across intake, coding, medical review, and submission.

At the same time, the landscape is rapidly evolving:

• 30–50% annual growth in AE volumes driven by digital channels, patient programs and real-world data.
• Increasing reliance on unstructured data sources such as EHRs, literature and social media.
• Rising regulatory expectations for data completeness, traceability and timeliness.
• Growing complexity of therapies, including biologics and gene therapies, making causality assessment more challenging.

Incremental automation approaches (RPA, rule-based tools) have delivered limited gains, as they address isolated tasks rather than the end-to-end workflow. As a result, pharmacovigilance functions face rising costs, operational bottlenecks, delayed reporting risks, and missed safety signals.

This creates a clear need to transition from reactive case processing to proactive, AI-driven safety intelligence.

### The Brillio Solution:

The solution deploys a coordinated system of specialized AI agents that mimic key pharmacovigilance roles and collaborate to process adverse events end-to-end.

Each AI agent contributes domain-specific intelligence:

• Intake Manager captures and structures adverse event signals from any source.
• Subject Historian builds a comprehensive patient safety profile using historical data.
• Drug Detective evaluates drug exposure, interactions, and causality.
• Infection Analyst identifies opportunistic infection risks and correlations.
• Severity Classifier determines seriousness, expectedness, and reporting priority.

These agents operate within a unified orchestration layer ingesting data from multiple sources (EHRs, literature, social media, call center interactions) enabling parallel processing and generating regulatory submission-ready ICSRs aligned with ICH E2B(R3) standards.

A critical differentiator is the Human-in-the-Loop (HITL) model, where all AI-driven outputs are reviewed and validated by safety professionals. Combined with full decision traceability and auditability, the solution ensures compliance with global regulatory requirements, including 21 CFR Part 11, GAMP 5, and emerging AI governance frameworks.