Understanding, predicting and optimizing complex chemical systems.
Many factors come together simultaneously in the development and optimization of complex products:
raw materials, physical properties, process parameters, various application requirements, multiple target windows.
These interrelationships can often only be mastered through the many years of experience of individual experts. Traditional software cannot map this complexity – and neither can AI.
Typical challenges
- Highly dimensional issues with many influencing variables
- Strongly expert-driven decisions (with fewer and fewer experts)
- Long development cycles with many experimental iterations
- Rising costs for raw materials and tests
- High market and regulatory pressure
- Existing data that can only be used to a limited extent
- Knowledge that is difficult to document and scale
Our solution
ai-predict integrates data, physico-chemical models and expert knowledge in a modular software platform.
Our Deep Knowledge Integration (DKI) approach creates AI twins that can analyze, predict and optimize complex relationships.
- Develop formulations faster
- Plan experiments in a more targeted way
- Understanding production processes better
- Systematically analyze trade-offs between properties, costs and raw materials
Our platform combines standard software with flexible customizability.
Thanks to a modular architecture, a wide variety of issues can be integrated – from data preparation and optimization algorithms to specialized physical models.
This creates fast, individual solutions within a scalable application:
Especially for specialty chemicals, but also far beyond!