Vigilance AI

Here is a concise, high-impact version of the project story, perfect for a quick read by judges. Vigilance AI Turning the chaos of global conversation into the clarity of strategic truth. Inspiration Once a drug hits the market, the feedback loop breaks. Real-world patients discuss side effects and efficacy on social media every second, but this data is unstructured and noisy. We built Vigilance AI to bridge the gap between raw patient voices and pharmaceutical strategy, turning scattered comments into actionable pharmacovigilance insights. What it does Vigilance AI is a real-time market perception engine.

Ingests Data: Continuously crawls Twitter/X, Reddit, and News APIs.
Analyzes Context: Uses NLP to distinguish Clinical Sentiment (e.g., "nausea," "recovery") from general noise.
Detects Risks: Automatically flags potential Adverse Events (side effects) for safety review.
Visualizes Truth: A dashboard tracking "Share of Voice," sentiment trends, and geographic hotspots. How we built it We architected a fully Serverless, Event-Driven solution for maximum scalability.
Infrastructure as Code: Provisioned entirely with Terraform on AWS.
Backend: Python scripts on AWS Lambda handle ingestion. Raw text goes to S3 (Data Lake), triggering processing functions.
AI/ML: Utilized spaCy and Transformers for Named Entity Recognition (NER) to identify drugs and symptoms.
Frontend: Built with React and Tailwind CSS, pulling real-time data from DynamoDB. Challenges we ran into
Medical Nuance: Teaching the AI that "This drug kills pain" is positive, but "This drug is killing me" is negative.
State Management: Debugging complex dependencies in Terraform state files.
API Limits: Handling strict rate limits from social platforms using exponential backoff strategies. Accomplishments that we're proud of
Speed: We achieved a <2 minute latency from a tweet being posted to it appearing on our dashboard.
Efficiency: The architecture scales to zero, costing nothing when idle.
Accuracy: Successfully detecting slang terms for side effects in unstructured text. What we learned
Data Quality: 80% of AI success is pre-processing and cleaning data to remove noise.
Cloud Architecture: The power of S3 triggers to decouple ingestion from processing.
Compliance: The critical importance of explainability in healthcare AI. What's next for Vigilance AI
Predictive Analytics: Forecasting sentiment trends before they happen.
Persona Detection: Distinguishing between posts from Doctors vs. Patients.
Global Reach: Adding multi-lingual support for European and Asian markets.