NEWS · CODING · #1206
AWS guide: building a contract-intelligence platform with Bedrock AgentCore
An AWS how-to describes an architecture for a React web app that converts portfolios of contract PDFs into structured, queryable data: AI extraction agents running via Amazon Bedrock (AgentCore) use Claude Sonnet to extract fields and Claude Haiku to verify them, Amazon Textract acts as a deterministic tiebreaker for signatures, results are stored in Amazon Aurora PostgreSQL, and users query via embedded analytics and a natural-language chat interface with real-time pipeline status over WebSocket.
KEY POINTS
- An AWS how-to describes an architecture for a React web app that converts portfolios of contract PDFs into structured, queryable data: AI extraction agents running via Amazon Bedrock (AgentCore) use Claude Sonnet to extract fields and Claude Haiku to verify them, Amazon Textract acts as a deterministic tiebreaker for signatures, results are stored in Amazon Aurora PostgreSQL, and users query via embedded analytics and a natural-language chat interface with real-time pipeline status over WebSocket.
- Shows a practical pattern combining LLM agents, deterministic vision (Textract), and a database to overcome RAG’s inability to perform portfolio‑wide aggregation.
- Building an AI-powered contract intelligence platform with Amazon Quick and Amazon Bedrock AgentCore
WHY IT MATTERS
Shows a practical pattern combining LLM agents, deterministic vision (Textract), and a database to overcome RAG’s inability to perform portfolio‑wide aggregation.