AI consulting for Chemistry and Materials R&D
We identify how you can utilize AI to accelerate your chemistry/materials R&D and increase your return on investment.
Want to accelerate your R&D but don't know where to start?
We will find out with you the best projects in your R&D portfolio that can be accelerated with Artificial Intelligence.
- Analyze your current and future R&D projects to determine whether they can benefit from using AI;
- Prioritize the most promising of these projects by estimating the value that could be created and the resources that would be required to integrate AI into them;
- Optimize data collection in current projects so that they are exploitable later by AI software when needed;
- Design future research projects that leverage AI;
- Identify untapped sources of data (eg. previous experiments, electronic notebooks, databases) that could be exploited further with AI to generate value;
- Create an AI strategy and a roadmap for your R&D laboratory or company.
How we work
Our consulting work is done by an AI expert in collaboration with your researchers and engineers.
We discuss with you:
- Your research topics;
- The objectives of your research;
- What parts of your projects you want to accelerate/augment;
- What data you already have (experiments, simulations, databases);
- What additional data can be generated or gathered if needed;
- Who will use the AI software and how;
- What investment you are ready to make to integrate AI in the projects;
- Any other relevant topic.
Once we have this global view of your R&D, we estimate the value that can be generated and the resources that are needed to integrate AI.
Finally, we write a report to summarize these findings and discuss it with you.
What you get
At the end of the AI consulting mission, you get a report that provides you with a clear vision of how you can increase the return on investment of your R&D by leveraging AI technologies.
Ideally, this consulting work should be done for several of your projects, and the results of the consulting study should be used to prioritize the most promising projects (higher reward, lower risk) for integrating AI technologies.
The next step is to start developing or adapting AI software for your most promising R&D projects.