AI-Driven Contract Analysis and Extraction at Scale

pricewaterhousecooper_/_pwc · Legal · 2026 · source Agentic: no · promotional bias: high · source-backed · extracted with prompt 0.5.2 (schema v2)

Generated from extraction.json — do not edit by hand. Every claim cites verbatim source evidence; stated means the quote directly asserts the claim, implied means it supports it indirectly.

Problem

Domain: Contract analysis and extraction

PwC customers needed to extract structured insights and answer questions over lengthy unstructured contracts at scale.

Constraints:

• legal/compliance reliance
• citations
• security
• large document volumes

Architecture

PwC’s AIDA is an AWS-native contract-insight platform. Authenticated users enter through edge security and Cognito/project roles, upload contracts and OCR outputs into S3, persist structured configurations and extracted insights in RDS, and use Bedrock/Knowledge Bases/Guardrails for LLM-powered rule extraction and natural-language Q&A with linked citations. QuickSight and downstream integrations expose throughput, status, analytics, and extracted insights.

Diagram confidence: explicit (topology described by the source).

flowchart LR
  edge-security-and-access["edge security and access"]
  cognito-and-roles["Cognito and roles"]
  aida-application["AIDA application"]
  s3-document-store["S3 document store"]
  rds-insight-store["RDS insight store"]
  bedrock-ai-services["Bedrock AI services"]
  quicksight-dashboards["QuickSight dashboards"]
  system-integrations["system integrations"]
  edge-security-and-access --> cognito-and-roles
  edge-security-and-access --> aida-application
  cognito-and-roles --> aida-application
  aida-application --> s3-document-store
  s3-document-store --> rds-insight-store
  aida-application --> rds-insight-store
  aida-application --> bedrock-ai-services
  bedrock-ai-services --> rds-insight-store
  aida-application --> quicksight-dashboards
  aida-application --> system-integrations

Components

Data flow

A user authenticates through AIDA’s edge/access layer, uploads contracts, and AIDA stores documents, OCR outputs, metadata, configurations, and extracted insights in S3/RDS. User-defined templates and extraction rules identify relevant contract context; LLMs interpret that context, return cited values or Q&A answers, and dashboards/integrations expose results to legal, procurement, and downstream systems.

Why this architecture

AIDA combines deterministic extraction structure with LLM interpretation because contract review needs scalable, repeatable fields and flexible natural-language answers, but legal/compliance use also requires traceability, role-based access, citations, guardrails, encryption, and human review before business or legal reliance.

Models used: LLMs via Amazon Bedrock · Amazon Bedrock Knowledge Bases · Amazon Bedrock Guardrails

Scale

• AIDA is intended to extract insights from thousands of contracts in parallel.
  “Use the custom extraction field and logic per document and extract insights from thousands of contracts in parallel with consistent accuracy.”article, stated
• Customer implementations reduced manual contract review time by up to 90%.
  “AIDA has helped reduce manual contract review time by up to 90%, helping teams to retrieve key information more quickly and shorten review cycles.”article, stated
• One major film and TV studio reduced rights research time by 90%.
  “One major film and TV studio reduced rights research time by 90%.”article, stated
• Dashboards track document volumes, OCR accuracy, extraction throughput, and processing status.
  “AIDA integrates with Amazon Quick Sight to visualize metrics such as document volumes, OCR accuracy, extraction throughput, and processing status.”article, stated

Alternatives considered (roads not taken)

Manual search and interpretation of contract language

Rejected because: AIDA reduces manual search/review by extracting structured insights and supporting natural-language questions with citations.

LLM output without source traceability

Rejected because: AIDA returns results with citations to original contract text so users can verify each insight.

Ungoverned AI-generated legal reliance

Rejected because: The source says safeguards and human review should be applied before legal/business reliance.

Patterns

rag-retrieval (document-intelligence)

AIDA answers contract questions with context-specific responses and linked source citations.

safety-policy-enforcement (enterprise-security)

The architecture layers identity, project-level permissions, encryption, and customer-controlled compliance safeguards.

Tradeoffs

Extraction strategy.

Chose rules plus LLM Q&A over keyword/pattern extraction alone.

Rationale: Rules provide repeatable structured fields while LLMs handle complex language and natural questions.

Gotchas (what bit them)

AI-generated contract outputs still need safeguards before business or legal reliance.

Root cause: Contracts contain sensitive, consequential information and LLM outputs may be incomplete or wrong.

Resolution: Apply access controls, citations, and human review workflows before relying on generated insights.

Evaluation

PwC reports customer implementation time-savings and use-case outcomes.

Metrics: up to 90% manual review reduction · 90% rights research reduction

Outcomes

• AIDA reduced rights research time by 90% for a major film and TV studio.

AWS/PwC solution post; marked non-agentic document-intelligence workflow rather than autonomous agent.