Building an AI-powered contract intelligence platform with Amazon Quick and Amazon Bedrock AgentCore
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This article details the architecture of an AI-powered contract intelligence platform built on AWS to address the limitations of standard Retrieval Augmented Generation (RAG) when handling large-scale data aggregation. The system utilizes Amazon Bedrock AgentCore with Strands Agents to extract key fields from PDF contracts using a dual-model verification approach, pairing Claude Sonnet for extraction and Claude Haiku for independent verification. To resolve discrepancies in signature detection, the pipeline employs Amazon Textract as a deterministic tiebreaker, ensuring higher accuracy than relying solely on language models. Extracted structured data is stored in Amazon Aurora PostgreSQL, enabling precise mathematical queries that semantic search cannot perform. Users interact with the platform via a React web application featuring embedded Amazon Quick Sight dashboards and a natural language chat agent powered by Amazon Quick. This unified interface allows stakeholders to query both portfolio-wide aggregates and specific document details seamlessly. The serverless design processes contracts rapidly with low variable costs, primarily driven by analytics licensing rather than inference expenses. Key takeaways emphasize extracting data for aggregation tasks, verifying results with independent models, and using deterministic services for visual or numerical precision.
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