Extracting contract insights with PwC’s AI-driven annotation on AWS

2026-04-29 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Intermediate, long

Summary

PwC's AI-driven annotation (AIDA) solution, built on AWS, extracts structured insights from contracts using large language models (LLMs) and rule-based extraction. Designed for legal, compliance, and procurement teams, AIDA interprets complex legal language, answers natural language questions about individual or multiple contracts, and provides context-specific answers with linked citations. The solution has reduced manual contract review time by up to 90% in customer implementations, such as a major film and TV studio. AIDA's architecture leverages AWS services like Amazon Bedrock for LLMs, Amazon S3 for storage, Amazon RDS for structured data, and Amazon OpenSearch Serverless for semantic search, ensuring scalability, security, and traceability. Key capabilities include customized data extraction via reusable templates, document-level chat, and global chat across documents, supported by Retrieval Augmented Generation (RAG) and Amazon Bedrock Guardrails for accuracy and safety.

Key takeaway

For legal and compliance executives managing high volumes of complex contracts, PwC's AIDA solution on AWS offers a significant opportunity to streamline workflows and reduce manual review time by up to 90%. You should evaluate AIDA's capabilities for automated extraction, natural language Q&A, and secure integration with existing systems to enhance decision-making and ensure compliance across your organization.

Key insights

PwC's AIDA on AWS uses LLMs and RAG to automate contract analysis, reducing review time by up to 90%.

Principles

• Combine LLMs with rule-based extraction for accuracy.
• Ground AI responses in source text using RAG.
• Apply fine-grained access control for sensitive data.

Method

AIDA processes contracts using OCR, extracts data via templates and LLMs on Amazon Bedrock, stores insights in Amazon RDS, and uses RAG with Amazon OpenSearch Serverless for context-aware Q&A.

In practice

• Define reusable templates for consistent attribute extraction.
• Use document-level chat for quick contract clarifications.
• Employ global chat to compare terms across multiple agreements.

Topics

• PwC AIDA
• Contract Analysis
• Large Language Models
• Retrieval-Augmented Generation
• AWS Cloud Architecture

Best for: Executive, CTO, VP of Engineering/Data, Legal Professional, Operations Professional, Consultant

Related on AIssential

• Grounding Your LLM: A Practical Guide to RAG for Enterprise Knowledge Bases — RAG systems combine LLMs with dynamic knowledge retrieval to provide accurate, auditable, and updatable answers for enterprise data.
• Invoice Reconciler - Built with LlamaCloud — AI agents can automate complex document reconciliation by comparing invoices against contracts using user-defined rules.
• 5 Cool Things I Did with Local Language Models — Local LLMs offer superior privacy, cost, and offline capabilities for sensitive data and proprietary workflows.
• AI-assisted Protocol Information Extraction For Improved Accuracy and Efficiency in Clinical Trial Workflows — AI-assisted RAG significantly boosts clinical trial protocol data extraction accuracy and efficiency over standalone LLMs.
• ExtractBench: A Benchmark and Evaluation Methodology for Complex Structured Extraction — LLMs struggle with complex, enterprise-scale PDF-to-JSON extraction, especially as schema breadth increases.
• Context-as-a-Service: Surfacing Cross-File Dependency Chains for LLM-Generated Developer Documentation — CaaS enhances LLM documentation agents by surfacing non-obvious cross-file dependencies, improving accuracy and efficiency.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.