Create a web-based AI-powered legal application. The primary goal is to automate and streamline the process of preparing an application for the Australian Partner Visa (specifically Subclasses 309/100 and 820/801).

The application should be built with an AWS backend and follow a modular architecture to ensure extensibility to other visa categories in the future. A key principle is maintaining a single source of truth for client data and incorporating human oversight at critical stages.

Design and describe the following core modules:

Document Intake Module:

Develop a user-friendly web interface for applicants and sponsors to upload required documents and input structured data based on an official Partner Visa checklist (referencing documents like passports, relationship statements, Form 888, evidence of relationship, financial evidence, character documents, travel history, family details).

Implement secure document uploads directly to AWS S3, tagging files with application ID and document type.

Provide structured form fields for data input (e.g., travel history, family members).

Include checklist tracking with status indicators (completed, pending, flagged).

Incorporate user authentication and session management (e.g., using AWS Cognito).

Document Validation Module:

Design a serverless backend service (e.g., AWS Lambda triggered by S3 events) to automatically validate uploaded documents.

Implement rule-based checks and leverage AI (AWS Textract for OCR/AnalyzeID, AWS Rekognition for image analysis) for specific validations:

Passport Validator: Check completeness, clarity, extract key fields (name, DOB, passport number, expiry).

Photograph Validator: Analyze couple photos (presence of two faces), check image quality (brightness, sharpness).

Text Message/Call Log Validator: Extract timestamps, names/numbers, check for context.

Form 888 Validator: Check for presence of key fields (applicant/sponsor names, witness details, how long known), ensure all pages are present, validate witness signature (note: certification of witness ID is no longer required as per the latest Form 888).

Other Evidence Validators: Basic checks for certificates, financial documents, personal statements (e.g., presence of names, dates).

Extract key metadata from documents (e.g., names, dates, passport numbers, witness details) and store it in a structured database (DynamoDB/RDS).

Record validation results (Valid, Flagged, Needs Attention) with reasons and extracted data/confidence scores.

Ensure the validator module is pluggable for future visa types.

Review and Notification Module:

Create a secure staff review dashboard (web portal) to view applications, submitted documents, validation statuses, and extracted metadata.

Enable staff to manually review documents, flag issues, and add comments.

Automate client notifications (e.g., via AWS SES/SNS) for missing or flagged documents, guiding them on necessary corrections.

Allow users to re-upload corrected documents.

Implement a mechanism for staff to mark an application as "Complete" and ready for generation only after all required items are resolved.

Consider admin controls for overriding validations.

Application Generation Module:

Develop a service to automatically compile and generate the draft visa application package once approved by the reviewer.

Implement form auto-filling for official forms (e.g., Forms 47SP, 40SP, 80, or their online equivalents) using the validated and extracted data.

Assist in generating narrative documents (e.g., relationship history summary, cover letter/document index) potentially using structured data or language models (with human review).

Compile the generated files (filled PDFs, narratives) and store them in S3 for final review and download.

Include quality checks to ensure all mandatory form fields are populated.

Design this module to be modular for different visa types.

Architectural Considerations:

Modularity and Extensibility: Design the system with clear separation of concerns, allowing new visa types to be added by updating configurations and adding specific validators/generators without rebuilding the core system.

Scalability: Leverage AWS serverless components (Lambda, S3, DynamoDB, API Gateway, Step Functions) for inherent scalability.

Security and Privacy: Implement robust security measures (HTTPS, Cognito, IAM roles, encryption at rest/in transit) and adhere to privacy compliance (Australian Privacy Act).

Data Mapping: Clearly define how data extracted from various documents maps to the fields required in the final application forms/structure.

Technology Stack: Recommend a suitable tech stack (e.g., React/Angular frontend, Python backend with relevant libraries for AI/document processing, AWS services).

Key Challenges and Refusal Prevention:

Address the complexity of proving "genuine and continuing" relationships by structuring evidence collection around the four pillars (financial, household, social, commitment) and flagging inconsistencies.

Implement stringent validation rules for critical documents like police certificates (correct type, validity period) and identity documents (certification where required, status proof for sponsors).

Provide clear user guidance on common pitfalls and how to address them (e.g., proving de facto relationships, explaining periods of separation).

Flag potential complex issues (character concerns, Schedule 3 issues) for human legal review.

Provide a detailed breakdown of the technical implementation for each module, including the specific AWS services used and how they interact. Outline the data flow through the system. Discuss potential challenges and how the design mitigates them.

Aim for a design that is not just a document checklist but a tool that assists users in building a compliant, consistent, and compelling application package.