Tetsuwan is developing AI agents that integrate natural language processing with laboratory robotics to autonomously execute complex wet lab experiments. This technology addresses the limitations of current lab automation, which requires extensive programming and lacks the ability to understand scientific intent, thereby enhancing reproducibility and efficiency in research.
Funding
$2.7M 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
Current lab automation solutions require extensive programming by specialized engineers, making them difficult to adapt to the high variety of experiments common in scientific research. This limits the effectiveness of automation for many tasks scientists face, hindering reproducibility and efficiency. Scientists often communicate experimental intent implicitly, a nuance that existing robotic systems cannot understand or translate into explicit actions.
Solution
Tetsuwan is developing AI agents that combine natural language processing with laboratory robotics to enable autonomous execution of complex wet lab experiments. The system decodes scientists' high-level instructions and translates them into actionable steps for lab robots, bridging the gap between experimental intent and robotic execution. By augmenting lab robots with automated calibration, liquid class characterization, and improved collision detection, Tetsuwan enables scientists to reliably use these robots for complex, high-differentiation tasks. This technology aims to create AI scientists that can assist human scientists by conducting more reproducible experiments, freeing them to focus on data analysis and hypothesis development, and ultimately automating the scientific method.
Target Audience
The primary target audience includes scientists and researchers in life sciences, particularly those in cancer therapeutics, precision medicine, and drug discovery, who seek to improve the reproducibility and efficiency of their experiments.
Features
- Natural language processing to decode scientific intent from high-level instructions.
- Two-way translation between experimental intent and robot execution using linear programming and rules-based systems.
- Automated calibration and liquid class characterization for lab robots.
- Improved collision detection for reliable autonomous operation.