We started Devano with a simple but ambitious mission: use modern AI to solve the everyday challenges that impact the pace of scientific discovery.

In the life sciences, data exists in a fascinating ecosystem of interconnected knowledge. Biomedical information naturally spans diverse formats, sources, and systems—reflecting the rich complexity of biology itself. This creates a fundamental challenge: questions that should be straightforward to answer often require hours of tedious manual work.

A researcher looking to compile "all human kidney studies using single-cell RNA-seq" faces a deceptively complex task. This information might exist across Gene Expression Omnibus (GEO), PubMed, supplementary materials, and other sources, but connecting the dots means manually:

• Sifting through free-text fields that might describe "kidney," "renal tissue," or "nephric samples"
• Cross-referencing papers to find crucial sample metadata not included in repository entries
• Determining experimental methods from inconsistently formatted protocol descriptions
• Understanding the access patterns and limitations of each data source

This manual connection-making steals time from the creative, high-value work that advances science.