pharma-data
(4)
Integral Privacy Technologies
Integral is a privacy engineering company that turns sensitive real-world data - starting with HIPAA-regulated healthcare records - into compliant, AI-ready datasets. Founded in 2022 by Shubh Sinha and John Kuhn, it pairs automated de-identification and continuous risk assessment with embedded statisticians and privacy engineers (its Forward Deployed Privacy Services) so enterprises and AI labs can use regulated data quickly without violating privacy law. The company positions itself as an independent privacy layer for the real-world data economy and has raised roughly $24.9M to date, including an $18M Series A closed in July 2026.
Century Health
Century Health is a New York-based health technology company that turns fragmented, messy electronic health record data into clean, structured, analysis-ready real-world evidence. Its AI-powered platform, CHARM (Century Health Abstraction & Retrieval Model), automates the curation, abstraction, and de-identification of clinical data to build disease-specific patient registries for pharmaceutical and life sciences research. Founded in 2023 by Vish Srivastava and Sanjay Hariharan, the company reports 97% accuracy against clinical expert judgment and raised a $5M seed round in 2026 led by Origin Ventures.
Vera Kutsenko
Vera Kutsenko is the founder and CEO of Atrix AI, a New York company building a trusted AI platform for life sciences that helps medical affairs teams at pharma and med-device companies turn messy real-world data into decisions they can act on. A Cornell-trained engineer who shipped code at Facebook and Uber, she is a two-time Y Combinator founder. Her previous startup, Neverland, was a venture-backed marketplace for houseplants and home gardening that raised $4.7M before closing. She is also a certified personal trainer who once ran her own plant nursery.
Novellia
Novellia is a New York-based digital health company that lets patients pull together their complete medical history from more than 50,000 U.S. healthcare systems into one free app, then choose whether to share de-identified versions of that data with drug companies and researchers. Using proprietary natural language processing, Novellia turns messy, unstructured clinical text - physician notes, lab narratives, diagnostic reports - into clean, research-ready longitudinal datasets. The result is a two-sided model: patients regain control of their records at no cost, while life sciences organizations get higher-quality, patient-consented real-world data than the fragmented claims-based datasets they buy today.