High-quality metadata is essential for large-scale biomedical analysis and AI model development.

But in public repositories like GEO, metadata is incomplete, inconsistently labeled, and scattered across GEO records, full-text publications, and supplementary PDFs.

Manual curation is slow, labor-intensive, and cannot scale. Existing automated methods offer speed, but often miss context, produce inconsistent outputs, and lack traceability.

Now you can get both - human-level metadata quality and automation at scale.

Join us for a behind-the-scenes look at a Multi-agent AI system that achieves:

• 93% recall across 23 key metadata fields including tissue, disease, cell line, donor ID, and treatment.
• Outperformance of GPT-4.1 single-pass prompting on accuracy, F1 score, and traceability.
• Curation of 4652 samples from 78 GEO datasets in days instead of weeks.
• 4x reduction in manual effort equivalent to replacing a 3-person expert team working for 1 month.
• Human-level accuracy, with 100% concordance on disease and 97% on gender based on CellxGene benchmarks
• Traceable records with field-level evidence attribution and confidence scores.