Amazon Web Services (AWS) has unveiled a novel approach to address the complex challenge of ensuring compliance across vast datasets, particularly in areas like apartment lease agreements. The new design pattern, dubbed ‘Adjudicated Query,’ integrates generative artificial intelligence within Amazon Quick with a deterministic rules engine to provide provable completeness and defensibility in compliance checks.
The Compliance Challenge at Scale
Managing tens of thousands of leases against constantly evolving state landlord-tenant laws presents a significant hurdle for compliance teams. Each jurisdiction has specific statutes regarding late fees, notice periods, and security deposits, which change according to legislative schedules. When regulations are updated, compliance departments must meticulously identify which leases no longer meet the requirements. While manual review by paralegals is feasible for small volumes, scaling this process software introduces a critical issue: the inability to independently verify the accuracy and completeness of the results.
This scalability problem gives rise to two essential properties: provable completeness, meaning every single record has been assessed and accounted for, and defensibility, the ability to rigorously defend findings against audits or legal challenges by detailing the exact rules, methods, and dates applied.
Traditional methods like Retrieval Augmented Generation (RAG) or Text-to-SQL, while enhancing data accessibility, fall short of these stringent requirements. RAG provides a ranked sample but cannot guarantee comprehensive coverage, and Text-to-SQL, despite its advantages, carries the risk of hallucinated predicates that can silently narrow the scope of analysis, leading to inaccurate population counts.
The Adjudicated Query Pattern Solution
The Adjudicated Query pattern establishes a controlled conversational layer over a reliable, non-AI-driven rules engine. In this architecture, generative AI’s role is confined to translating natural-language questions into calls for a predefined set of operations and narrating the outcomes. Crucially, it does not write queries, alter data populations, or make final compliance decisions.
Underpinning this layer is a rules engine where laws are managed as versioned data, akin to database entries, rather than executable code. This engine utilizes generic comparison operators and lacks jurisdiction- or topic-specific branching, meaning legal changes are implemented as simple row edits in the rulebook.
Every compliance sweep generates a completeness receipt, an assertion that the sum of compliant, in-breach, ambiguous, and unreadable records equals the total number of scanned documents. This invariant ensures that no record is overlooked and is validated before data persistence. The conversational interface then presents counts, the receipt, and a sample of findings, while the full dataset, potentially numbering in the tens of thousands, is accessible via a drillable dashboard.
Key Architecture Components
The reference architecture leverages several AWS services. Amazon Quick serves as the user interface, offering both a chat agent for natural language queries and an Amazon Quick Sight dashboard for detailed analysis. User authentication and authorization are managed by Amazon Cognito, which issues OAuth tokens to secure requests passed through Amazon API Gateway to an AWS Lambda function. This Lambda function hosts the Model Context Protocol (MCP) server and the rules engine. Data is stored in Amazon Aurora Serverless v2, accessible via the RDS Data API. Amazon Bedrock is utilized exclusively for the exploratory clause-search function, employing Amazon Titan Text Embeddings V2 for similarity ranking and Anthropic Claude Sonnet 5 for qualitative assessments, but it is not involved in official compliance determinations. Amazon Quick Sight connects directly to the Aurora store, ensuring both interfaces operate from a single source of truth.
Deployment and Practical Application
A complete reference implementation, including synthetic data and acceptance tests, is available on GitHub. The deployment process involves setting up AWS services like Amazon Cognito, API Gateway, Lambda, Aurora Serverless v2, and Amazon Quick Sight. Users can then interact with the system through Amazon Quick chat to perform compliance sweeps. For instance, a query such as “Which Texas leases violate the late fee cap? Use rules effective 01/01/2026” would invoke the sweep_compliance tool. The system then provides a summary of noncompliant findings, the completeness receipt, and a link to a detailed Amazon Quick Sight dashboard where every lease-rule pair can be examined, offering a full audit trail for defensibility.
The design emphasizes non-negotiable rules: rules are data, natural language does not reach SQL, determinism precedes AI, and completeness is exact. Findings are append-only to prevent tampering. Security is integral, with authenticated access, secure secret handling, least-privilege IAM permissions, and network isolation for data paths. The Adjudicated Query pattern is particularly suited for domains where missed records incur liability, findings may be scrutinized later, governing logic is externally controlled, and the record set is enumerable, such as insurance claims, sanctions screening, and export control.
❤️ Support Independent Journalism
Your contribution keeps our reporting free, fearless, and accessible to everyone.
Or make a one-time donation
Secure via Razorpay • 12 monthly payments • Cancel anytime before next cycle
(We don't allow anyone to copy content. For Copyright or Use of Content related questions, visit here.)
The Chenab Times News Desk
