Ganymede provides a modular platform that connects laboratory instruments, ELNs/LIMS, and applications to standardize and unify scientific data. This integration enables the automation of repetitive analysis and the acceleration of lab workflows across various life science applications. The platform delivers clean, aggregated data to support advanced analytics and AI integration for faster scientific insights.
Funding
$15.2M raised to dateRaised 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.
Founders
Product
Problem
In biotech and pharmaceutical labs, scientific data is often trapped within disparate instruments, electronic lab notebooks (ELNs), and laboratory information management systems (LIMS), leading to data silos and manual data handling. This fragmented data landscape hinders automation, increases the risk of errors, and prevents scientists from fully leveraging their data for analysis and AI applications.
Solution
Ganymede provides a low-code platform that unifies lab instruments, ELN/LIMS, and data infrastructure into a single FAIR (Findable, Accessible, Interoperable, and Reusable) data cloud. The platform automates data flow, standardizes data formats, and ensures data integrity, saving scientists time and enabling them to focus on research. By connecting every instrument and automating analysis, Ganymede eliminates tedious data tasks, reduces data entry errors, and provides a foundation for AI-driven insights. The platform's modular design allows for flexible integration with existing lab systems and supports a wide range of scientific workflows, from small molecules to cell and gene therapies.
Target Audience
Ganymede targets biotech and pharmaceutical companies, including scientists, lab managers, IT professionals, and R&D leaders, who seek to streamline their lab operations, improve data quality, and accelerate drug discovery.
Features
- Connectors for a wide range of lab instruments, ELNs, and LIMS, with tools to build new connectors as needed
- Automated data capture and parsing into structured, FAIR-compliant tables
- Flow-based web IDE for writing Lab-as-Code automation and building pipelines
- Version control with Git and an append-only data model for data integrity
- Business intelligence (BI) tools for data visualization and analysis
- Ability to virtualize scientific analysis applications within the platform's UI
- Compliant data storage with context, traceability, and versioning