Workflow Intake Analysis Demo v1.0.0

Workflow Intake Analysis Demo v1.0.0

This first public release presents a reproducible Python and SQL workflow for validating inconsistent synthetic intake while preserving uncertainty and traceability.

What it demonstrates

• translates requirements and acceptance criteria into executable validation rules;
• separates valid records from records that require human review;
• preserves raw and normalized values so transformations remain explainable;
• validates cross-record references across the complete synthetic batch;
• reconciles SQLite, SQL exports, and generated reports;
• exposes the same validation core through a bounded local API; and
• documents its schemas, API contract, Postman checks, automated tests, CI, design decisions, and limitations.

Verified release result

• 30 synthetic intake records: 21 valid and 9 review-required;
• 17 canonical findings and 20 stage events;
• 420 trace rows;
• 15 generated SQL exports;
• 6 generated reports with 12 of 12 reconciliation checks passing;
• merged-main Linux CI: 83 tests passed and 1 Windows-only wrapper test skipped;
• local Windows validation: all 84 applicable tests passed; and
• Postman/Newman: 7 requests, 14 assertions, 0 failures.

AI-assisted work

AI-assisted tools supported brainstorming, drafting, refactoring, test ideas, and consistency review. Human judgment controlled the requirements, implementation choices, review decisions, validation, public claims, and final release.

Scope and limitations

The project uses synthetic data and runs locally. It is a professional-quality demonstration, not a claim of live integration, production authentication, enterprise scale, or measured business outcomes. Review-required records deliberately remain unresolved when the source context does not support a fact.

Reproduce the result

Follow the repository quick start and validation instructions at tag v1.0.0. The tag and this release are bound to the normally merged, CI-validated main tree.