AI-powered metadata correction and harmonization

This article demonstrates an AI-powered metadata correction and harmonization system built on AWS that standardizes datasets across different sources using LLMs and intelligent validation.

• Schema alignment uses Amazon Bedrock LLMs to recognize industry-specific synonyms and detect complex column relationships beyond string matching
• Metadata field validation categorizes errors into required fields, enumerated values, and pattern violations with structured error reporting
• Recommendation engine layers embeddings, fuzzy matching, contextual inference, and LLM fallback to balance cost, performance, and accuracy
• Human-in-the-loop workflow keeps researchers in control while automation accelerates metadata corrections through browser interface
• Agent-driven workflows enable autonomous metadata correction at scale using Model Context Protocol for tool access and reasoning
• Governance framework addresses data integrity, architecture decisions, responsible AI guardrails, and security for sensitive genomic data

The system reduces metadata standardization bottlenecks in biomedical research by combining automation with human oversight, enabling organizations to scale data harmonization as volumes grow.