BioStack Funding: Raises $9.5 Million in Seed Round Led by Peak XV

Healthcare technology startup BioStack has raised $9.5 million in seed funding led by Peak XV, with participation from Afore Capital, DCVC, Emerson Collective, SV Angel, Verdict Capital, Streamlined Ventures and Y Combinator.

Announced on October 22, 2026, the funding will support BioStack’s expansion across the United States and international markets, development of its technology platform, and growth of its engineering and research teams.

Founded by CEO Sanat Mishra and CTO Parth Patwa, BioStack is building data infrastructure that connects fragmented clinical information across healthcare systems and turns it into longitudinal datasets for medical research and healthcare AI development.

The company says its network spans the US, India, Brazil and Argentina, with access to more than 100 million de-identified patient records. BioStack is part of Y Combinator’s Spring 2026 batch.

BioStack Connects Fragmented Clinical Data for Medical AI

Healthcare information is often distributed across hospitals, providers and different software systems. A patient’s clinical history can include notes, laboratory results, medical imaging, medications, procedures, treatment histories and outcomes stored in separate systems.

BioStack is developing workflows that bring these different data points together to create a more complete, longitudinal view of a patient’s medical journey. The company says this infrastructure can support applications including medical research, drug development, disease modelling, biomarker discovery, patient stratification and treatment-response analysis.

The company also creates proprietary datasets from de-identified clinical information collected through provider systems. These datasets are intended to support the training and evaluation of healthcare AI models that need to work with multiple types of clinical information rather than isolated medical records.

BioStack says it has assembled several hundred thousand X-rays, MRI and CT scans, alongside pathology and other clinical records. The company says these datasets can be used to develop AI systems that study disease progression, treatment pathways and patient outcomes.

The startup is also developing workflows tailored to individual healthcare providers and their data-handling protocols. This allows different institutions to maintain their own requirements while contributing clinical information to broader research and AI-development workflows.

BioStack’s current positioning also extends beyond datasets to healthcare simulation environments. Y Combinator describes the company’s platform as creating realistic healthcare environments where AI systems can use real-world clinical data to practise medical decision-making before deployment.

In India, BioStack says it is helping connect cancer patients with an investigational cancer vaccine programme. The company says the initiative can connect relevant clinical data with biotechnology development while potentially providing eligible patients access to investigational treatments.

Funding to Expand BioStack’s Clinical Data Network

The new funding will be used to expand BioStack’s network of healthcare data partnerships in the US and other international markets, improve its technology platform and increase its engineering and research capabilities.

The company’s model depends on bringing together clinical information from multiple healthcare systems while maintaining the privacy and data-handling requirements associated with patient information. BioStack says it creates provider-specific workflows and de-identifies information before creating datasets for research and AI applications.

Its reported network of more than 100 million de-identified patient records spans four countries: the United States, India, Brazil and Argentina. However, the figure represents the overall network of records available to the company and does not mean every record contains every type of clinical or imaging data.

The startup’s approach is aimed at addressing a limitation in healthcare AI development: models can require access to clinical information that captures what happened to patients over time, including treatments, follow-up events and outcomes.

For AI developers and researchers, BioStack intends to provide datasets that can be used both to train and evaluate medical AI systems. Its longer-term proposition is to provide infrastructure that allows models to work with the complexity of real-world clinical histories rather than isolated medical records.