Catalog content quality
AI-assisted Title Quality Engine
Arabic-first title quality engine that enriched 10M+ titles and improved quality scores across categories.
Problem
Title quality baseline was low and uneven, limiting discoverability and conversion.
Solution
Led scoring methodology and AI-assisted generation workflow with supporting attribute pipelines.
Outcome
10M+ titles enriched, title quality lifted to 93–96%, and $8M annualized gross sales impact.
Architecture
A placeholder implementation path that can be expanded with screenshots, data contracts, system diagrams, and measurable results as the project matures.
01
Quality scoring
02
Attribute pipeline
03
Title generation
04
Validation checks
05
Bulk enrichment
06
Post-launch monitoring
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
- Title quality score
- OPS uplift
- Category coverage
- Sales impact
Product Role
- Identified quality gap
- Co-designed scoring logic
- Drove cross-team implementation