Materials and Chemicals

Combining human and machine intelligence to develop and manufacture materials & chemicals better, cheaper, and faster.

Materials and chemicals are the foundation of every physical product, from batteries, to aeroplane papers, and medicine. Addressing the changing climate will require us to transition to new and sustainable materials, but developing and commercializing new materials, chemicals, and related products is very expensive. Nuron is using AI to help companies to address these challenges faster, better, and efficiently.

Departments

Research

Explore insights for innovative discoveries and trends. Conduct fundamental experiments and develop breakthrough chemicals.

Product Development

Create advanced formulations that meet market demands. Transform ideas into concrete products, maintaining quality and innovation.

Pilot and Scaleup

Take projects from pilot to full-scale production. Data-driven optimizations ensure a smooth and efficient transition.

Production

Monitor operations, ensure efficiency and quality standards. Data-driven optimizations support continuous and reliable production.

Engineering & Maintenance

Apply analytics to improve predictive maintenance and process efficiency. Maintain reliable facilities for uninterrupted operations

Marketing & Support

Understand customer needs and trends. Create marketing strategies and offer support based on actionable insights.

Selected Use Cases

CASE STUDY

65% reduction in R&D experiments by centralizing global data and using predictive machine learning.

CASE STUDY

80% reduction in time to generate QA, QC, and analytical reports, resulting in better quality products, reduced failures, and lower product development cost.

CASE STUDY

Saving 90% of manual data analysis work by automation, standardization, and democratization of complex data analysis.

CASE STUDY

Implementation of best and secure data management and usage practices in R&D, pilot, and manufacturing. Elimination of tech transfer gaps.

CASE STUDY

Elimination of failure risks by predicting experiment outcomes and finding similar experiments previously completed.

CASE STUDY

Use of predictive AI and ML to predict properties of new materials and design new materials.