From RAG to Structured Extraction: Building an AI Contract Intelligence Pipeline on AWS

AI contract extraction pipelines are moving from pure retrieval‑augmented generation (RAG) to a hybrid model that pulls key fields from PDFs into a relational store. This change lets engineers answer portfolio‑wide questions—such as total spend or upcoming expirations—without the hallucinations that arise when a chat model only sees a handful of text chunks.

Why RAG Alone Falls Short

RAG splits each document into vector chunks and returns the top‑k most relevant pieces for a query. That works for pinpoint lookups, e.g., “What are the payment terms in Contract X?” but fails when the answer requires aggregating data across hundreds of contracts. The model never sees the full dataset, so totals, counts, or comparisons are incomplete or incorrect.

Architecture Overview for AI Contract Extraction

The solution is a React front‑end hosted on AWS that orchestrates an event‑driven pipeline:

1. Contract PDFs land in an Amazon S3 bucket, triggering the processing flow.
2. An extraction agent built on Claude Sonnet (via Amazon Bedrock) reads each PDF and emits eight predefined fields with confidence scores.
3. A verification agent using Claude Haiku re‑processes the same file and cross‑checks the extraction.
4. If the two agents disagree on signature detection, Amazon Textract provides a deterministic visual check.
5. Verified records are persisted in Amazon Aurora PostgreSQL.
6. Real‑time pipeline status is streamed to the UI over WebSocket, and users can query the data through embedded dashboards or a natural‑language chat interface.

The architecture is serverless, allowing parallel processing of many contracts and automatic scaling as the portfolio grows.

Implementation Considerations

Key implementation points include:

• Defining the eight contract fields and confidence thresholds to drive verification logic.
• Choosing Bedrock model versions (Sonnet for extraction, Haiku for verification) that balance cost and accuracy.
• Configuring S3 event notifications to reliably invoke the processing components.
• Designing the Aurora schema to support efficient aggregation queries (SUM, COUNT, MAX, etc.).
• Integrating Textract only for the edge case of signature disagreement to limit additional compute expense.

Operational and Security Implications

Operating this pipeline introduces several practical concerns:

• Monitoring model invocation latency and cost, especially when processing large batches of PDFs.
• Ensuring S3 bucket policies restrict access to authorized roles, as the raw contracts contain sensitive business data.
• Auditing the verification step to detect systematic extraction errors before they propagate to the database.
• Backing up Aurora snapshots and configuring encryption at rest to protect the structured contract repository.
• Providing observability for the WebSocket status channel so UI users receive accurate pipeline health information.

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What This Means For Practitioners

Adopting a structured extraction pipeline replaces unreliable RAG‑only answers with deterministic, queryable data. Engineers should evaluate model selection, cost, and scaling patterns, while also hardening storage and verification steps to keep contract data secure and trustworthy.