Project case study
Managed AI Software Team / Internal Delivery Platform
A structured AI-assisted delivery platform for moving software requests through intake, planning, implementation, review, and release artifacts.
Current status
Active portfolio case study; implementation details are summarized for public review.
Technologies
- Next.js
- TypeScript
- React
- Supabase
- Drizzle
Problem
AI-assisted software work can become hard to evaluate when prompts, plans, generated files, review notes, and release context are scattered across conversations. The project focused on turning that work into a clearer delivery system where each step can be inspected before it moves forward.
Solution
- Designed intake flows for capturing module requests, delivery context, and implementation constraints.
- Created structured outputs for module briefs, architecture proposals, implementation plans, generated file bundles, review reports, and release packages.
- Modeled delivery artifacts so generated work could be reviewed by a human before it was accepted.
- Built a full-stack application foundation with typed data access and maintainable UI patterns.
Technical highlights
- Next.js and TypeScript application structure for a maintainable delivery workflow.
- Supabase-backed persistence with Drizzle-managed schema modeling.
- Human-in-the-loop review stages for planning, implementation, and release artifacts.
- Separation between intake, planning, implementation, review, and packaging concerns.
Tradeoffs
- Prioritized traceability and reviewability over a fully automated black-box workflow.
- Kept AI framed as engineering leverage inside a structured process rather than treating generated output as automatically correct.
- Accepted more explicit workflow steps so decisions and artifacts remain easier to audit.
What this demonstrates
- Practical AI-assisted engineering workflow design.
- Full-stack product architecture for internal tools.
- Ability to turn ambiguous delivery needs into structured software systems.
- Strong judgment around automation boundaries and human review.