Features
Automated data entry from source to EDC
Context-aware field mapping for study, visit, patient
Quality checks before proposing entries
Flag low-confidence fields for manual review
Source traceability for each data field
Works across browser-based sources and EDCs
No custom integrations or heavy setup needed
Paper/PDF source support via waitlist (coming soon)
Reduces transcription errors
Works alongside eSource and EDC systems
Contract directly with sites, not sponsors
AI-powered suggestions with FDA alignment
Browser extension for Chrome (likely)
Proprietary Recursion OS platform for drug discovery
Automated wet lab generating millions of cell experiments per week
Over 50 petabytes of biological and chemical data
NVIDIA BioHive-2 supercomputer for AI model training
Hit identification to IND-enabling studies with improved speed and cost
Potential first-in-class treatments in oncology and rare diseases
Advanced pipeline with phase 1/2/3 clinical trials (REC-4881, REC-3565)
Target identification and validation using machine learning
Generative AI for molecule design and optimization
De-identified patient data integration for precision medicine
Strategic partnerships with pharma and tech leaders
End-to-end drug development capability from preclinical to pivotal studies
Fit-for-purpose dataset generation including ADME and safety profiling
Real-world evidence generation through clinical trials
Continuous feedback loop from experiments into AI models
