BioStack

AI labs and biotech startups building healthcare models need domain-specific clinical data such as EHRs, imaging, ECGs, labs, notes, and patient outcomes to train and improve their systems. This kind of data is harder to find than general datasets, often unstructured, and not ready for machine learning pipelines. Teams building reinforcement learning environments also need data-rich reward functions, which require richer and more context-heavy clinical data than what is typically available. Without access to this kind of structured, high-quality healthcare data, teams are limited in how well they can train, fine-tune, or evaluate their models.

BioStack sources and structures healthcare data across EHR, labs, imaging, ECGs, notes, treatments, and patient outcomes, converting it into ML-ready datasets. The data supports pre- and post-training of models, causal inference work, and building reinforcement learning environments with data-rich reward functions. BioStack also annotates novel and public data and provides multi-agent reasoning infrastructure. The company positions this offering for AI labs looking to improve healthcare model performance and for biotech startups that want custom data to fine-tune and deploy industry models. Prospective clients can book a free consultation to get started.