About This Architecture

Multi-tier pharma research platform ingests FDA and patent data via EC2-based scrapers and Lambda incremental fetchers, storing raw HTML/PDF snapshots in S3. AWS Bedrock processes documents for parsing, entity extraction, and similarity scoring, populating a PostgreSQL RDS master data model. Dual Node.js/Express app servers behind Route 53 serve web, mobile, and tablet clients, enabling researchers to query structured regulatory and IP intelligence. This architecture demonstrates cost-effective AI/ML integration for life sciences teams needing real-time access to evolving regulatory datasets. Fork this diagram on Diagrams.so to customize scraper schedules, swap Bedrock models, or add ElastiCache for query acceleration.

People also ask

How do I build an AWS architecture for scraping and analyzing FDA and patent data with AI?

Use EC2 and Lambda to scrape FDA and Google Patents, store raw files in S3, process with AWS Bedrock for parsing and extraction, then load structured data into RDS PostgreSQL. Serve via Node.js app servers behind Route 53 for multi-client access.