Key challenges
The organization managed a rapidly expanding repository of promotional and non-promotional content across brands, markets, and channels. Manual tagging processes, inconsistent metadata quality, and rigid taxonomies limited content discoverability, reduced content reuse, and increased compliance risk. The absence of a feedback-driven improvement mechanism further constrained classification accuracy over time.
Large content volumes across markets and brands
Manual, time-intensive tagging processes
Low discoverability and content reuse
Inconsistent metadata quality
Limited feedback-driven model improvement
Compliance risks from inaccurate or incomplete metadata
The solution
Centralized Content Foundation
Fractal implemented a centralized content repository integrated with enterprise taxonomies and historical metadata. This created a consistent foundation for automated content classification and discovery across commercial operations.
AI-Driven Content Classification
Content assets were processed through AI models that automatically generated tags, classifications, and metadata recommendations with confidence scores.
Scalable AWS-Based Architecture
The platform leveraged Amazon Bedrock for content understanding and metadata generation, Amazon SageMaker for model development and refinement, Amazon OpenSearch Service for indexing and discovery, and AWS Lambda for workflow orchestration and automation.
Human-in-the-Loop Learning
An embedded review process allowed human experts to validate and refine AI-generated recommendations. Feedback was continuously captured and used to improve model performance and alignment with business needs.
1
Automated Classification
AI-generated content tags
Automated metadata recommendations
Confidence-based classification support
2
Enhanced Discoverability
Improved content indexing
Better search and retrieval experiences
Faster access to relevant content
3
Improved Governance
Human validation workflows
Oversight of AI recommendations
Stronger metadata quality controls
4
Greater Content Utilization
Increased opportunities for content reuse
Consistent classification practices
Reduced manual tagging effort
Automated Classification
AI-generated content tags
Automated metadata recommendations
Confidence-based classification support
Enhanced Discoverability
Improved content indexing
Better search and retrieval experiences
Faster access to relevant content
Improved Governance
Human validation workflows
Oversight of AI recommendations
Stronger metadata quality controls
Greater Content Utilization
Increased opportunities for content reuse
Consistent classification practices
Reduced manual tagging effort
