Science Machine logo

Science Machine

AI agents for automating biomedical data analysis and report generation, from mass spectrometry to flow cytometry.

Visit website

Overview

ScienceMachine is an AI-powered platform designed to automate complex biomedical research workflows, taking scientists from raw data all the way through to final reports — end-to-end and without manual intervention. Trusted by researchers worldwide, ScienceMachine delivers speed, accuracy, and cost-effectiveness, enabling life sciences teams to stay competitive in an increasingly data-driven research environment.

Unlike generic chatbots, ScienceMachine deploys purpose-built AI agents that learn the specific research context, guidelines, and preferences of each team. The platform integrates deeply with existing data sources, tools, and workflows, and is backed by world-class expert support from scientists who understand the underlying research domains. It is designed to be both transparent and reproducible, giving users full access to every line of code and every decision the AI makes.

Key Workflow Capabilities

  • Mass Spectrometry Processing: Automated analysis of mass spectrometry data to identify key peptides, reducing manual processing time significantly.
  • Flow Cytometry Processing: Automated analysis of flow cytometry data to identify and characterize cell populations.
  • Report Generation: Automated creation of reports and presentation slides directly from research data.
  • Result Interpretation: Deep, automated interpretation of experimental results grounded in referenced scientific literature.
  • Data Analysis and Visualization: Automatic formatting, analysis, and visualization of research results for clear and actionable outputs.

Core Platform Features

  • Your Own AI Agent: The AI is tailored to each team's specific research, guidelines, and preferences, functioning as a dedicated research assistant rather than a one-size-fits-all tool.
  • Deep Integration: ScienceMachine connects directly to existing data sources and tools, fitting seamlessly into established research workflows.
  • Secure and Private: Data never leaves the customer's cloud environment and is never shared with third parties. Full privacy and encryption are enabled by default.
  • Transparent and Reproducible: Every line of code and every AI decision is accessible to users, ensuring full reproducibility and scientific control.
  • World-Class Expert Support: A dedicated team of domain experts provides high-quality support, ensuring outputs meet the specific scientific standards of each customer.

Enterprise-Grade Security

  • Never used for training: Customer inputs and outputs are owned entirely by the customer; ScienceMachine only provides the AI tooling.
  • Enterprise encryption: All data is encrypted using enterprise-grade security practices, trusted by dozens of life sciences companies.
  • Never shared with others: Each customer's data and computation are fully isolated, with no cross-contamination between users or organizations.

Pricing and Plans

  • Free Plan ($0, Free Forever): Includes standard data processing, generic capabilities, and basic security and privacy. Does not include expert support, custom integrations, or large file sizes.
  • Enterprise Plan (Custom Pricing): Provides a full-scale AI agent tailored to the customer's specific research needs. Includes increased compute resources, custom integrations, dedicated expert support, enterprise-grade security, enterprise encryption, and guarantees that data is never used for training or shared with others.

Research and Publications

  • BixBench: A comprehensive benchmark for LLM-based agents in computational biology, developed in collaboration with FutureHouse — establishing standards for AI agent performance in biomedical research.
  • BioML-bench: An evaluation framework for AI agents applied to end-to-end biomedical machine learning, authored by Henry E. Miller, Matthew Greenig, Benjamin Tenmann, and Bo Wang.
  • Published Research Written with ScienceMachine: A 2025 paper titled A novel glucose beta-hydroxybutyrate combination improves hypoglycaemia recovery and patient-reported outcomes in type 1 diabetes, authored by D. Russell-Jones MBBS et al. and published in Diabetes, Obesity and Metabolism, was produced with the assistance of the ScienceMachine platform.

ScienceMachine positions itself at the frontier of AI automation in life sciences, having established benchmarks for AI agents in biomedical research and demonstrated real-world impact through peer-reviewed publications. Teams looking to accelerate their R&D workflows can begin with a free demo requiring no credit card, or engage the enterprise plan for a fully customized, secure, and expert-supported deployment.