Intellegens utilizes machine learning to optimize the design of experiments in materials and chemicals, significantly reducing the number of required experiments by up to 90%. This technology effectively handles sparse and noisy data, enabling faster and more cost-efficient R&D processes across various industries, including manufacturing and pharmaceuticals.
Funding
$730K 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 materials science and chemical engineering, the design of experiments (DOE) is often a resource-intensive process, requiring numerous physical experiments to optimize formulations or processes. Traditional methods struggle with sparse and noisy data, leading to inefficient R&D workflows and increased costs.
Solution
Intellegens offers Alchemite™, a machine-learning platform designed to optimize the design of experiments, particularly in materials, chemicals, and manufacturing. Alchemite™ leverages advanced machine learning algorithms to extract maximum value from sparse, noisy, and real-world data, enabling users to achieve R&D project goals with significantly fewer experiments. The platform identifies key relationships within the data, allowing researchers to focus on the most impactful experiments and reduce experimental workloads by as much as 90%. By building predictive models, Alchemite™ facilitates virtual experiments, shortens development times, and accelerates innovation in various industries.
Target Audience
The primary target audience includes R&D teams, materials scientists, chemical engineers, and manufacturing professionals in industries such as chemicals, materials science, FMCG, life sciences, and manufacturing.
Features
- Adaptive design of experiments that reduces the number of required experiments by 50-80%
- Advanced machine learning algorithms capable of handling sparse and noisy data
- Predictive modeling for materials, chemicals, paints, drugs, foods, and personal care products
- Identification of key relationships between process parameters and material properties
- Graphical analytics, including importance charts and sensitivity plots, for increased transparency
- Uncertainty quantification to guide robust decision-making
- Integration of microstructural image data to improve model accuracy
- Support for additive manufacturing process parameter optimization