The absence of pre-existing data shouldn’t slow you down. Instead of extracting insights from existing data, Aixelo uses AI decision making to create the data you need to move forward and solve material challenges faster.

Your lab team shouldn’t need to be AI experts to leverage the power of many innovative algorithms. Through a combination of strategic guidance and software, we make AI algorithms accessible to R&D teams. This removes the steep learning curve and makes it easier to integrate AI into the lab.

Your lab teams already follow a specific methodology to discover better materials, so new technology with a foreign process causes friction. That’s why Aixelo is intentionally designed around the processes your teams already use, allowing for seamless integration into their workflow.

Data analytics is great for getting scientific insights and learning from data. But if the solution is outside of the existing knowledge or database, you’re stuck.

Our closed-loop AI algorithms serve as a self-learning copilot for experimental design. The Aixelo software system improves itself with each new experiment to make better and better decisions, enabling scientists to quickly hit their project goals.

In R&D environments, the context of data generation is constantly changing. Your team spends time and effort defining data formats only for them to be outdated by the time you implement.

We believe that AI only works in an R&D setting at scale if the system can handle unstructured data. This is why Aixelo pioneers large language models that automatically translate unstructured user input into machine-readable formats.

In-house digital teams build internal competencies but often lack the IT infrastructure to deploy their AI technology. We provide an open AI enterprise platform that enables your data science teams to integrate their algorithms into our scalable system to ensure your AI expertise finds its way into day-to-day R&D operations.