PwC's AIDA Tool Cuts Contract Review Time by 90% on AWS

Contract analysis has long been a bottleneck for legal, compliance, and procurement teams. Important insights sit buried in lengthy, unstructured agreements, and as contract volumes grow, finding specific clauses becomes increasingly difficult to scale. PwC has deployed a new solution to address this friction, built in partnership with Amazon Web Services.

The tool, called AIDA (AI-driven annotation), extracts structured insights from contracts through a hybrid approach combining rule-based extraction with natural language queries. Using large language models (LLMs), AIDA interprets complex legal language and extracts insights based on defined rules. Users can ask natural language questions about individual contracts or across multiple documents within a project and receive context-specific answers supported by linked citations.

In customer implementations, AIDA has helped reduce manual contract review time by up to 90%, according to the AWS blog post co-written with PwC. The solution demonstrates three core capabilities: template-based extraction, document-level chat, and global chat across documents.

One major film and TV studio reduced rights research time by 90% using the system. In the Media & Entertainment sector, AIDA helps content producers and distributors unlock the overall value of their IP by extracting and analyzing rights information from license agreements. It summarizes rights such as broadcast, streaming, theatrical, and derivative, enabling faster decisions on spin-offs, sequels, and global distribution.

The architecture behind AIDA illustrates how the components work together to securely process, analyze, and deliver insights from complex contracts using AWS cloud-native services. Each component is designed to process contracts at scale while maintaining security, traceability, and performance.

At the edge layer, requests pass through AWS WAF for threat filtering, then through a Network Load Balancer to a reverse proxy server (NGINX), which manages SSL termination, routing, and policy enforcement before forwarding to Amazon Elastic Container Service (Amazon ECS). Data in transit is encrypted using TLS 1.2 or higher, including user connections through HTTPS and internal service-to-service communication between Amazon ECS, Amazon Relational Database Service (Amazon RDS), Amazon Simple Storage Service (Amazon S3), Amazon Bedrock, and other AWS services.

Authentication is handled through Amazon Cognito, integrated with enterprise identity providers like Microsoft Entra ID or Okta to secure access at scale. AIDA applies fine-grained access control through both application-level and project-level roles, so administrators can manage user access and permissions centrally.

After authentication, AIDA stores uploaded documents, Optical Character Recognition (OCR) outputs, and associated metadata in Amazon S3, providing a durable and cost-effective way to manage large volumes of contract data. Structured data, configurations, and extracted insights persist in Amazon RDS, so users can query and retrieve insights effectively for analytics and integration.

Amazon S3 buckets are encrypted at rest using AWS Key Management Service (AWS KMS) keys. The solution provides capabilities that can support organizational security, compliance, and risk management requirements, though customers remain responsible for configuring and operating the solution to meet their specific compliance obligations.

As AIDA processes potentially sensitive contractual data, appropriate safeguards and human review workflows should be applied prior to business or legal reliance on AI-generated outputs (a necessary caveat that every enterprise AI deployment should include, frankly).

The solution offers customized data extraction enabled by user-defined rules and custom templates. Users can extract insights from thousands of contracts in parallel with consistent accuracy. Natural language Q&A across documents allows users to ask questions and receive context-specific responses with linked citations to the source documents.

Integration with model systems enables AIDA to connect with contract management systems and document repositories to retrieve source data and deliver extracted insights. This matters because contract data doesn't live in isolation—it needs to flow into downstream systems for analytics, reporting, and decision-making.

AIDA can support scalable contract analysis across a wide range of industries, including Media & Entertainment and Real Estate, and competencies like Procurement, Legal, and Compliance. The PwC official page notes that the firm's deep industry experience is built directly into the technology, enabling navigation of more complex data extractions and customization of data models to fit specific needs.

What makes this approach different from pure LLM-based solutions is the hybrid architecture. Deterministic rule-based extraction handles the predictable, structured elements of contracts, while LLMs interpret nuanced language and provide natural language interfaces. This blend aims to improve recall on complex clauses while preserving structured outputs for downstream systems.

Practitioners implementing similar systems typically must integrate document ingestion, embeddings or retrieval indexes, prompt engineering for context windows, and human-in-the-loop validation to preserve accuracy and auditability. The linked citations feature is critical here—it provides provenance for every answer, which legal and compliance teams will demand.

Contract review is a common enterprise bottleneck. Combining rule-based extraction with LLMs aims to improve recall on nuanced clauses while preserving structured outputs for downstream systems. Because contractual data is sensitive, integrating security controls, provenance for citations, and configurable compliance workflows remains a core operational requirement.

Indicators to monitor include retrieval and citation precision under real workloads, degradation from model drift, integration effort with contract lifecycle management systems, and the maturity of human review workflows for edge cases. Observers will also watch how vendors surface provenance and audit trails to satisfy legal and compliance teams.

The claimed 90% reduction in manual review is significant for deployments but comes from a vendor blog rather than independent evaluation. Individual client results may vary based on contract types and the volume of contracts available, as PwC notes in its documentation.

Whether organizations actually achieve these savings depends on how well the solution integrates with existing contract management systems and how much customization is required for specific industries. The technology is ready, but the real work begins when legal teams start validating outputs against their own compliance standards.

Artūras Malašauskas is an AI Systems Integrator with 20+ years of production-grade web engineering experience. He has designed, shipped, and scaled enterprise Python/PHP systems for logistics, SaaS, and public-sector clients. For the past year, he has focused exclusively on AI integrations: deploying open-source LLMs, building generative media pipelines (image, audio, video), and engineering multi-agent workflows for real production environments. His standard: reproducibility, security, cost-efficient inference—no vaporware. He documents and evaluates emerging AI tooling, separating verified capabilities from marketing noise. Technical editor at: muza-ai.eu, ai-verslas.lt, ai-naujinos.lt Connect on LinkedIn