Marketplace catalog systems
Product Variation Intelligence
ML clustering + LLM validation + confidence scoring to improve product family and variation quality across categories.
Problem
Noisy seller data and category-specific variation logic made scalable product grouping unreliable.
Solution
Built a multi-tier architecture: clustering, category rules, low-confidence LLM validation, and human resolution.
Outcome
Unlocked large commercial upside while balancing automation with review controls.
Architecture
A placeholder implementation path that can be expanded with screenshots, data contracts, system diagrams, and measurable results as the project matures.
01
Attribute ingestion
02
Embeddings + clustering
03
Category variation rules
04
LLM validation
05
Confidence scoring
06
Human review
Product Artifacts
Sanitized examples to demonstrate product thinking and execution style when proprietary materials cannot be shared.
- PRD outline (problem framing, success metrics, rollout plan)
- Workflow wireframe / journey snapshot
- Evaluation rubric or quality checklist
- Operational metrics dashboard mock
Metrics to Track
- Broken grouping rate
- Cluster precision
- Review deflection
- Product detail page conversion on fixed groups
Product Role
- Owned V2 strategy
- Secured engineering/science alignment
- Shipped confidence-driven operational model