BioStack Platforms
BioStack Platforms provides a cloud-native data platform that aggregates, curates, and standardizes biomedical datasets into ML-ready formats for AI-driven drug discovery and healthcare model development. The platform includes automated annotation, causal inference tools, and a multi-agent reasoning layer for building reinforcement-learning environments, with SDKs for Python, R, and JavaScript. It also offers a medical-imaging navigation environment built from 172,353 individually addressable images across 2,166 series for RL post-training evaluation.
- Drug Discovery & Therapeutics
- Biotechnology
- Artificial Intelligence
- Software Only
Funding
Raised to date
$10MRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
across 1 round
+2$9.5M · Seed
Announced Oct 2026
Disclosed cumulative: $9.5M
Founders
Founder details are not available yet.
Product
Problem
Biotech and AI research teams often spend extensive time and resources aggregating heterogeneous pre-clinical and clinical data from disparate repositories, leading to high acquisition costs, inconsistent formatting, and delayed model development for drug discovery and safety assessment.
Solution
BioStack delivers a cloud-native data platform that aggregates, curates, and standardizes biomedical datasets into ready-to-train formats for machine-learning pipelines. The service includes automated annotation, custom dataset synthesis, and a multi-agent reasoning layer that enables causal inference and the creation of reinforcement-learning environments for post-training evaluation. Users access the data via RESTful and SDK-based APIs, allowing seamless integration with popular frameworks such as PyTorch, TensorFlow, and Ray RLlib. Built-in version control and provenance tracking ensure reproducibility, while secure, HIPAA-compliant storage protects sensitive patient information. By centralizing high-quality, domain-specific data, BioStack reduces acquisition spend and accelerates the iteration cycle of AI-driven drug discovery projects.
Target Audience
Primary customers are biotech startups, pharmaceutical R&D divisions, university research labs, and AI technology firms developing bio-focused models that require high-quality, ML-ready datasets and causal reasoning tools.
Features
- Automated ingestion pipeline that normalizes raw omics, imaging, and perturbation screens to FAIR-compliant schemas (e.g., OBO, CDISC)
- Scalable annotation engine leveraging active-learning and domain-expert validation to enrich datasets with phenotype, assay, and ADMET labels
- On-demand custom dataset generation service that merges public and proprietary sources, applies statistical matching, and outputs TensorFlow Record or Parquet bundles
- Multi-agent reasoning infrastructure supporting causal graph construction, counterfactual simulation, and reward-function synthesis for reinforcement-learning tasks
- Medical-imaging navigation environment built from full CT, MRI, and PET/CT studies, exposing 172,353 individually addressable images across 2,166 series for RL post-training evaluation
- SDKs for Python, R, and JavaScript plus OpenAPI endpoints for bulk download, incremental updates, and real-time query filtering
- Integrated data versioning and lineage tracking with Git-like commit history and checksum verification
- Enterprise-grade security: end-to-end encryption, role-based access control, and audit logging meeting HIPAA and GDPR requirements