AI Inventory & Operations Dashboard
Full-stack inventory workflow concept with AI reorder suggestions in development
Full-stack dashboard combining real-time inventory tracking with an OpenAI forecasting layer now in development — built to prove end-to-end app architecture and practical AI integration.
Problem
Small-to-mid-size businesses rely on spreadsheets and gut instinct for inventory decisions, leading to stockouts or costly overstock.
Goal
Build a modern dashboard that combines real-time inventory tracking with LLM-powered forecasting to surface actionable restocking recommendations.
What I Built
A Next.js full-stack app with role-based access and a Prisma-backed PostgreSQL data layer, designed around OpenAI demand analysis that will generate plain-language reorder suggestions — structured-output prompts are currently in testing.
Key Features
- Real-time inventory grid with search, filters, and bulk actions
- Planned: AI demand forecasting via OpenAI structured outputs
- Automated low-stock alerts with configurable thresholds
- Role-based dashboard views (admin, warehouse, viewer)
- CSV import/export for legacy system compatibility
Challenges
- Designing structured prompts that return reliable JSON for forecasting
- Balancing real-time updates with efficient database queries
- Building a permission model flexible enough for multiple org sizes
Outcome
Architecture, schema design, and role-based access are complete. OpenAI integration is the active workstream — demand forecasting prompts are in structured-output testing.
Current Status
Dashboard workflow, schema design, and product architecture are built. Live AI actions are the next phase.