Features
Natural language querying of clinical data
Automated literature review for evidence grounding
Cohort building with temporal data mapping
Causal DAG builder with user review
Statistical Analysis Plan (SAP) generator
Data extraction and transformation to OMOP CDM
Sandboxed execution of statistical code
Report writer producing publication-ready outputs
Target trial emulation for comparative effectiveness
Survival analysis with Kaplan-Meier and Cox models
Propensity score matching and weighting
Sensitivity analysis and empirical calibration
Self-improving agents that learn dataset structure
Auditable trace from raw data to results
Support for R, Python, and SAS code generation
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
