Packaging Intelligence MVP
AI-assisted packaging analysis tool that turns product specs into sustainability and cost insights.
- Role
- Lead Builder
- Timeline
- 2024
Overview
Project summary and scope.
A product MVP that helps teams evaluate packaging options by extracting structured data from product inputs, validating key attributes, and surfacing sustainability and cost-oriented insights. Built to move quickly from ambiguous stakeholder requests to a demo-ready workflow.
My Contributions
Key areas of ownership and delivery.
- Led MVP scoping from ambiguous requirements to a demo-ready product surface
- Built extraction and validation workflow with structured schema outputs
- Implemented review cards for human verification of AI-generated fields
- Created CSV export flow for downstream analysis placeholders
- Prepared stakeholder demo narrative and technical documentation
Core Features
Primary capabilities delivered in this project.
- SKU and product spec ingestion with structured extraction
- Validation workflow for critical packaging attributes
- Comparison cards for sustainability and cost indicators
- CSV export for analysis and reporting placeholders
- Demo-ready UI for stakeholder walkthroughs
Architecture
How the system is structured at a high level.
The MVP uses a Next.js frontend for user interaction, API routes for orchestration, and schema-validated LLM calls for extraction. Validation layers flag low-confidence fields before export. Data persistence supports iteration on product specs and review history.
Impact
Outcomes and value delivered.
- Delivered an end-to-end MVP from concept to demo-ready prototype
- Reduced time to produce structured packaging comparisons for review
- Established a reusable pattern for LLM extraction plus human validation
Challenges
Constraints and difficulties encountered during delivery.
- Extracting reliable structured data from inconsistent supplier formats
- Designing validation UX that catches errors without blocking demo velocity
- Scoping MVP features tightly enough to ship while leaving room to expand
Tradeoffs
Key decisions and the reasoning behind them.
- Favored schema-constrained extraction over open-ended chat for consistency
- Used placeholder sustainability scoring models to unblock product demos
- Kept backend lightweight rather than building full supplier integrations early
Future Improvements
Next steps that would strengthen or extend this work.
- Integrate live supplier and materials databases
- Add role-based review and approval workflows
- Expand scoring models with verified sustainability data sources
- Support batch ingestion for large SKU catalogs
Problem and Solution
Problem
Product and packaging teams needed a faster way to compare packaging options without manually parsing specs, spreadsheets, and inconsistent supplier data. Existing workflows were slow and hard to validate.
Solution
Delivered an MVP with LLM-assisted extraction, validation checkpoints, and exportable outputs. The product flow guides users from SKU input through structured packaging attributes to reviewable recommendations.
Tech Stack
Technologies used across this project.
- TypeScript
- Next.js
- OpenAI API
- Node.js
- Prisma