AI-Assisted Content Categorization
Impact Statement
94% REDUCTION IN MANUAL CATEGORIZATION TIME WITH 95% ACCURACY.
Role
FULL-STACK DEVELOPERTimeline
September 2023 - January 2024
CHALLENGE & OVERVIEW
Chrome extension and backend system for automated content categorization using LLM-powered classification with human review workflows.
SYSTEMATIC APPROACH
DISCOVER
Comprehensive analysis of existing systems, workflows, and performance baselines
MODEL
Architecture design and technology selection with structured content modeling
SPECIFY
Detailed implementation planning with migration strategy and testing framework
BUILD
Incremental implementation with continuous validation and performance optimization
PROVE
Impact measurement, optimization, and long-term maintenance planning
BUSINESS IMPACT
Successfully modernized platform serving 2M+ monthly users with significant improvements in performance, authoring experience, and technical maintainability. Editorial team now focuses on content strategy rather than technical workarounds, enabling higher content quality and publication velocity.