From Siloed Data to Unified Intelligence
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Biological data is vast but disconnected, living in isolated silos that prevent a holistic understanding of patient health.
Unstructured EHR data
DNA/RNA sequences
MRIs, CT Scans, X-Rays
Protein expression data
Data is treated as a curation/storage problem (efficiency) instead of an intelligence/knowledge problem (efficacy).
Simply aggregating data isn't enough. It's like sequencing the genome and expecting it to instantly cure cancer—the real work is in connection and interpretation.
This leads to isolated models across an organization that don't learn from each other, creating redundancy and fragmented insights.
Oncology
Immunology
Cardiology
Our platform semantically integrates all data at the patient level, creating an active learning system where every data point enriches the whole.
By understanding biology at a fundamental level, we can unlock previously impossible applications and make predictions across different diseases.
This is possible. By analyzing the tumor microenvironment in cancer patients, we can discover novel targets and pathways to develop new treatments for autoimmune diseases like lupus or rheumatoid arthritis.
From unified biological intelligence to transformative clinical applications
Indication selection based on pre-clinical data
Phase-III insights using Phase-I data
Designing Better Phase-III trials from phase-II data
Designing more successful renal trial using NSCLC trial data
