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Litegrav

Litegrav provides hardware and software platforms that enable high‑throughput testing of biological processes in simulated microgravity and other extreme environments.

Tallinn, EstoniaFounded 20219200+ followers
Updated 1 month ago

Funding

Funding not disclosed

Funding rounds are not available yet.

Founders

Founder details are not available yet.

Product

Problem

Researchers lack scalable, flexible platforms to test biological processes under simulated microgravity and other extreme conditions, making it difficult to generate reproducible data for space‑related and Earth‑based biotechnology applications.

Solution

Litegrav offers a modular hardware and software system that lets scientists conduct high‑throughput experiments in programmable simulated microgravity, partial gravity, and vibration environments. The platform supports standard labware and can run up to 768 samples per unit, with the ability to synchronize multiple units for parallel screening. Plug‑in modules such as in‑situ imaging, radiation exposure, and automated fluid handling can be added without replacing the core hardware. Adaptive machine‑learning algorithms provide real‑time modeling and outcome prediction, enabling on‑the‑fly adjustments and reducing trial‑and‑error cycles. The closed‑loop control software ensures precise acceleration and vibration profiles, delivering reproducible conditions across experiments.

Target Audience

Primary customers are academic and industry laboratories conducting biotechnology, regenerative medicine, or materials science research that requires simulated microgravity or extreme environment testing, including space‑flight research programs and biomanufacturing developers.

Features

  • All‑in‑one hardware that switches between 2D/3D clinostats, random positioning, orbital shaking, or custom motion trajectories in minutes
  • Parallel processing capability of up to 768 samples per unit and synchronization of eight units for large‑scale screening
  • Modular plug‑ins for real‑time microscopy, radiation dosing, fluid exchange, and other experiment‑specific tools
  • Adaptive machine‑learning engine that predicts experimental outcomes in real time and optimizes protocol parameters
  • Software‑defined closed‑loop control of acceleration and vibration to maintain stable, reproducible motion profiles
  • Scalable architecture that starts with standard labware and expands to larger bioprocessing units as research needs grow
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