We backed IOMED because real-world clinical data was locked in unstructured physician notes and fragmented EHRs. The team built a data-space platform that uses NLP and automated terminology mapping to extract, structure, and harmonize clinical data into the OMOP standard, enabling hospitals and life-science companies to unlock research-ready real-world evidence for patient recruitment, feasibility studies, and clinical evidence generation while preserving privacy and compliance.