ai-predict.de provides a modular software platform that combines data, physico-chemical models, and expert knowledge to create AI-Twins for analyzing, predicting, and optimizing complex chemical systems. The platform's Deep Knowledge Integration (DKI) approach enables faster formulation development, targeted experiment planning, and systematic analysis of trade-offs between properties, costs, and raw materials. It delivers proof-of-concept in under two weeks and a minimum viable product with real value in under eight weeks.
- Artificial Intelligence
- Data & Analytics
- Biotechnology
- Chemical Technology
- New Materials
- Software Only
Funding
Founders
Product
Problem
Developing and optimizing complex chemical products involves numerous interacting factors—raw materials, physical properties, process parameters, application requirements, and multiple target windows. These high-dimensional relationships are typically manageable only through the long-term experience of individual experts, which is becoming scarce. Companies face long development cycles with many experimental iterations, rising raw material and testing costs, and existing data that is only partially usable.
Solution
ai-predict provides a modular software platform that integrates data, physico-chemical models, and expert knowledge through its Deep Knowledge Integration (DKI) approach. The platform creates AI-Twins that can analyze, predict, and optimize complex relationships in chemical systems. It combines standard software with flexible customization, allowing for rapid, individual solutions within a scalable application. The platform transforms company data into "KI-Ready" format within days, delivers a proof of concept in under two weeks, and achieves a minimum viable product with real value in under eight weeks.
Target Audience
Primary customers are companies in the specialty chemicals and pharmaceutical industries, with applicability extending to mechanical engineering, automotive, aerospace, and energy sectors. The platform serves organizations dealing with complex product development and production processes who need to leverage their existing data for competitive advantage.
Features
- Deep Knowledge Integration (DKI) combining domain expertise, physics, chemistry, and data science for explainable AI solutions
- Modular architecture supporting diverse applications from data preparation to optimization algorithms and specialized physical models
- AI-Twins that analyze, predict, and optimize complex chemical formulations and production processes
- Customizable KI-Pipelines that generate models from current data within minutes
- Interfaces to existing software and processes, including automation and process optimization tools
- Support for challenging data situations through adaptive AI-based Design of Experiments (DoE) planning and simulated data generation