Atinary offers a no-code AI platform, Self-Driving Labs®, that utilizes machine learning and robotics to optimize experimental workflows in research and development. This technology accelerates the discovery of new materials and molecules, significantly reducing the time and cost associated with traditional R&D processes.
Funding
$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
Traditional research and development processes for new materials and molecules are often slow, expensive, and inefficient, hindering the pace of scientific discovery and innovation. Optimizing experimental workflows and managing the vast amounts of data generated present significant challenges for scientists.
Solution
Atinary offers Self-Driving Labs®, a no-code AI platform that leverages machine learning and robotics to optimize experimental workflows, accelerate R&D, and drive the discovery of new materials and molecules. The platform enables scientists to plan and optimize experiments, analyze data, and make data-driven decisions through a user-friendly interface. By integrating AI, automation, and cloud computing, Atinary streamlines R&D processes, enhances throughput, and reduces the time and cost associated with traditional experimentation. The platform also facilitates data management and knowledge sharing, centralizing data for machine learning applications.
Target Audience
Atinary's primary customers include scientists and researchers in pharmaceuticals, biotechnology, chemicals, and climate tech industries seeking to accelerate the discovery of new materials and molecules.
Features
- No-code AI platform for experiment planning, optimization, and data analysis
- Proprietary algorithms for optimizing and applying constraints across experiments
- Integration of AI, robotics, and cloud computing for automated workflows
- Data management tools for centralizing data and facilitating knowledge sharing
- AI-driven Design of Experiments (AI-DoE) for generating high-quality, reproducible, and ML-ready datasets
- Connectivity and communication between different hardware and software systems, allowing AI to drive and orchestrate experiments with minimal human intervention