Frequently Asked Questions About ai-predict
What can our software do, how does the collaboration work, and what benefits can you expect?
Here, we answer the most important questions about our modular AI decision-making platform, its potential applications, as well as data, integration, security, and costs.
Our current focus includes paints, varnishes, and coatings. The underlying technologies can also be used for other complex formulation and process tasks and expanded accordingly.
What is ai-predict, and what is the platform used for?
ai -predict is a software company that supports industrial formulation development using artificial intelligence, physicochemical models and expert knowledge.
The modular AI -core platform enables the use of existing measurement data and expertise for the analysis, simulation, and optimization of complex formulations.
A particular focus is placed on paints, coatings, and color development, especially when factors other than color—such as curing behavior, reactivity, or process conditions—must be taken into account.
Using the Deep Knowledge Integration (DKI) approach, ai-predict combines various knowledge sources to plan development trials more effectively, evaluate formulation alternatives, and enable well-informed decisions.
What exactly can ai-predict do, and what added value does the software offer?
ai -predict helps companies to better understand, simulate and specifically optimize complex formulations and processes.
Our modular AI -core platform combines existing data, physicochemical models, expert knowledge, and artificial intelligence. This enables us to analyze relationships, predict properties, virtually evaluate different solution options, and derive well-founded recommendations for action.
A particular focus is placed on simultaneously optimizing multiple target variables, such as color, product properties, process conditions, and costs.
The benefits: fewer experimental trials, shorter development processes, and better, more transparent decisions.
Existing software and specialized modules can be customized and expanded to meet specific customer needs. This results in solutions that are scalable, ranging from individual use cases to more comprehensive decision support.
For which industries and applications is ai-predict suitable?
ai -predict is aimed at companies that want to develop, analyze or optimize complex formulations, materials and industrial processes – especially where several properties and influencing factors need to be considered simultaneously.
Our current areas of focus include paints, varnishes, and coatings, among others. A specific example of its application is the simultaneous optimization of color tone and UV curing, taking into account relevant formulation and process parameters.
Other interesting areas of application include, for example, effect paints, colored plastics, and other complex formulations in the specialty chemicals industry.
Thanks to our modular ai -core platform, the solution approach is not limited to individual industries. Existing models and software components can be adapted, combined, and extended with new specialist modules.
What matters is not so much the industry as the problem at hand: wherever data, physical and chemical relationships, and expert knowledge can contribute to better decisions, our approach offers potential.
What does “Color Plus” mean, and which properties can be optimized together?
With “Farbe plus,” we take an approach that goes beyond traditional color optimization: Color is not considered in isolation, but rather in conjunction with other product properties, formulation parameters, and process conditions.
In the case of paints, varnishes, and coatings, factors such as color, opacity, film thickness, curing, raw material usage, and cost can interact with one another. Optimizing one property can affect other properties.
Our modular ai -core platform makes it possible to investigate such relationships by combining physicochemical models, measurement data and AI, and to jointly evaluate multiple target variables.
A specific example of this application is the joint optimization of color shade and UV curing. In this process, the formulation and process conditions are considered together to identify technically feasible solutions and potential conflicts of interest.
The goal is not only to achieve the best possible color match, but also to make an informed decision regarding the formulation that is most suitable overall.
Which characteristics can be optimized together depends on the available data, suitable models, and the specific technical requirements.
What sets ai-predict apart from traditional AI models and conventional formulation software?
Traditional AI models learn patterns from existing data and can, for example, predict certain product characteristics. Conventional formulation software often focuses on individual tasks such as color calculation, formula adjustment, or raw material selection.
ai -predict goes a crucial step further: Our modular ai-core platform combines data, physicochemical models, expert knowledge and AI to provide more comprehensive decision support.
With our Deep Knowledge Integration (DKI) approach, we make targeted use of known physical relationships rather than having to learn them anew solely from data. AI complements this knowledge, particularly in cases where relationships or model parameters are not yet sufficiently understood.
A key difference lies in multi-target optimization: multiple properties, objective functions, and technical constraints can be considered together. This approach also takes into account interactions, conflicting objectives, and physical limits.
Rather than simply predicting what will happen with a specific formulation, ai-predict helps determine which technically feasible formulations best meet the desired objectives.
This results not only in predictions, but also in transparent solution spaces, simulations, and concrete recommendations for action, tailored to the specific task at hand.
Which software modules are already available, and which ones can be added?
Our modular ai -core platform forms the technological basis for the analysis, simulation and optimization of complex formulations and industrial processes.
We already have Reusable software and model components, which can be combined, adapted, and expanded depending on the specific application.
These include, in particular:
- Data Management: Import, processing, analysis, and visualization of various data sources.
- Physicochemical models: These include spectral color models as well as models for selected material and process properties.
- AI and Statistics: Methods for Modeling, Parameter Estimation, and Property Prediction.
- Optimization: Calculation and evaluation of formulation and process alternatives, taking into account defined objectives and constraints.
- Decision Support: Simulations, result visualization, and transparent recommendations for action, such as AI-optimized DOE.
A particular focus is placed on color models and their relationship to other properties, such as UV curing.
Depending on customer requirements, existing components can be customized, and additional specialized modules can be developed. In doing so, we aim to integrate new features into the platform in a way that maximizes reusability.
Your advantage: We build on existing technologies and expand them in a targeted manner, rather than having to develop a completely new software solution for every new challenge.
How do simulation, AI twins, and decision support work?
With ai -predict, companies can virtually examine formulations and processes before conducting costly laboratory tests or production adjustments.
Simulations make it possible to predict the effects of changes in formulations, raw materials, or process parameters on specific product properties and to compare different variants with one another.
An AI twin digitally replicates selected characteristics and relationships of a real product or process. To do this, we combine physicochemical models, existing measurement data, expert knowledge, and appropriate AI methods. The virtual twin can thus serve as a basis for simulations and optimizations.
The decision support goes even further: Instead of merely calculating the expected properties of a formulation, it can search for and evaluate suitable formulation and process alternatives for defined targets.
In doing so, multiple objectives, technical constraints, and potential conflicts of interest can be taken into account.
The result: Companies can compare alternative solutions more effectively, make more informed development decisions, and reduce the number of experiments required.
The types of predictions and optimizations that are possible, and how reliable they are, depend on the specific model, the available data, and the area of application under investigation.
What data does ai-predict need, and what happens if there is only a small amount of data available?
The data required depends on the specific issue at hand. Typical examples include formulation data, raw material properties, measured values, laboratory results, and information on production and process conditions.
What matters is not just the volume of data, but above all its quality and relevance.
Unlike purely data-driven AI approaches, ai -predict relies on the combination of data, physicochemical models, and expert knowledge. Our Deep Knowledge Integration (DKI) allows known relationships to be directly incorporated into the modeling. This reduces the need for extensive training data in suitable applications.
Even when data sets are limited or incomplete, we assess which analyses and simulations are already possible and which additional measurements are most likely to yield the greatest insights.
Our approach: We start by reviewing your existing data and your specific challenge. Together, we assess which models would be appropriate to use and whether additional data is needed.
This often makes it possible to identify early on which solution is technically and economically promising—without having to accumulate large amounts of data first.
How are expert knowledge, physical-chemical models, and AI combined?
ai -predict follows a hybrid approach: We combine scientific models, practical expert knowledge and artificial intelligence to map complex relationships as efficiently and comprehensibly as possible.
We refer to this approach as Deep Knowledge Integration (DKI).
Physicochemical models describe known laws and relationships, such as those between formulation components, material properties, and process conditions. The expertise of experienced employees supplements these models with practical insights, technical limitations, and application-specific requirements.
AI and statistical methods are used in particular in situations where relationships are not yet fully understood, model parameters must be determined from measurement data, or additional influencing factors must be taken into account.
Combining these methods makes it possible to make targeted use of existing knowledge, rather than having to learn all the relationships solely from large amounts of data.
The benefit for you: Models can perform effectively even with limited data, take physical relationships into account, and support transparent simulations and optimization decisions.
The choice of which methods to combine depends on the specific research question and the available data.
How reliable and transparent are the results?
The reliability of simulations and AI predictions depends on various factors, particularly the quality of the available data, the models used, and the specific area of application.
ai -predict relies on the combination of physicochemical knowledge and data-driven AI methods. This allows known laws to be taken into account, results to be checked for their physical plausibility, and relationships to be better understood.
An important part of our approach is validating the models using existing measurement data and targeted experiments. In doing so, we examine how well the predictions align with real-world results and within what limits the models can be reliably applied.
Our goal is to provide decision support that is as transparent as possible: Relevant influencing factors, model assumptions, technical constraints, and potential uncertainties should be clearly identifiable.
Important: Even high-performance models do not provide absolute certainty. New formulations or process conditions may need to be verified experimentally.
The benefit for you: Instead of relying solely on past experience or AI predictions that are difficult to understand, you’ll have a systematic framework for evaluating alternative solutions, better assessing risks, and making informed decisions.
Can ai-core be integrated into existing software, measuring devices, and processes?
Yes. Our modular ai -core platform is designed to integrate existing data sources, software solutions, and industrial processes into customer-specific applications.
These include , for example , colorimeters, laboratory and production data, formulation databases, and existing analysis and management systems.
Depending on the technical infrastructure, data can be exchanged and processed using existing file formats, database interfaces, or appropriate application programming interfaces (APIs).
We distinguish between importing existing measurement data and establishing a direct connection to measuring devices or other systems. The available options depend on the available interfaces and the respective manufacturer specifications.
Our goal is to complement existing workflows in a meaningful way, rather than unnecessarily replacing tried-and-true systems.
As part of our joint analysis, we will examine what data and systems are already in place, how they can be integrated, and which additional features offer the greatest benefit.
This allows ai -predict to be gradually integrated into existing development, laboratory and production processes.
For which user groups is the software suitable? Is training required?
ai -predict is aimed at different user groups in research, development, laboratory, production and technical management.
Our goal is to make complex AI technology accessible even to those without specialized AI or programming knowledge.
Depending on the task and the user’s role, different functions and user interfaces can be provided:
- Laboratory and Development: Analyzing formulations, simulating properties, and evaluating new formulation options.
- Research and subject matter experts: examining models, analyzing influencing factors, and tackling complex optimization problems.
- Production and Quality Assurance: Evaluate process parameters, investigate deviations, and support decision-making.
- Management and project leaders: Clearly assess results, conflicting objectives, and opportunities for improvement.
Our modular ai -core platform enables the development of application-specific, browser-based user interfaces that can be tailored to individual requirements.
Whether and to what extent training is necessary depends on the application and its complexity. We assist users with implementation and provide the necessary understanding of the models and results.
Our philosophy: Technology should be tailored to the users’ tasks and knowledge, not the other way around.
How does the collaboration work, from the demo to the pilot project?
The collaboration with ai -predict begins with a free and non-binding initial consultation . During this meeting, we will introduce you to our ai -core platform and existing software modules, and learn about your specific challenges.
Our goal is to work with you to determine how AI, physicochemical models, and existing data can help solve your challenges.
The typical process consists of five steps:
- Understanding the Challenge: We’ll discuss your specific issues, targets, and desired improvements.
- Evaluating Data and Potential: Together, we review existing measurement data, formulations, and process information. For suitable use cases, we can conduct initial analyses or simple simulations within the agreed-upon scope.
- Develop a solution: We assess which existing modules can be used and what adjustments or additional models are required.
- Implementing a pilot project: Based on jointly defined goals, deliverables, and success criteria, we develop and validate an initial application-specific solution.
- Expand gradually: Once validation is complete, the solution can be integrated into existing processes and expanded to include additional functions, features, or user groups.
The benefit for you: You’ll first get to know our technology and the possible solutions before deciding on a follow-up project.
The initial consultation and potential assessment are free of charge within the agreed-upon scope. More extensive development projects and pilot projects are tailored to individual needs and offered on a case-by-case basis.
How quickly can initial results be achieved?
How quickly initial results are available depends on the specific research question, the available data, and the complexity of the required models.
A key advantage of ai-predict is our modular AI core platform: We can draw on existing software and model components and tailor them specifically to the respective requirements. This means that not every solution has to be developed from scratch.
Even during the free, no-obligation initial consultation, we can discuss your challenges, evaluate your existing data, and outline possible solutions.
For suitable use cases, we can then develop initial analyses, modeling approaches, or simple simulations. We will work with you to determine the scope and effort involved.
For follow-up pilot projects, we define a clear scope of work, specific success criteria, and a realistic timeline. In doing so, we assess, step by step, how reliable the results are and what practical benefits the solution offers.
Our goal: To produce meaningful results as early as possible, limit technical risks, and demonstrate the economic value of a solution before making major investments.
How are sensitive data, formulas, and models protected?
Recipes, measurement data, process information, and company-specific expert knowledge are valuable and often confidential corporate resources. The responsible handling of this data is of paramount importance to ai -predict .
Our modular AI -core platform enables us to tailor data processing, modeling, and software integration to our customers’ technical and organizational requirements.
Before beginning a collaboration, we work together to determine what data is needed, how it will be processed and stored, who is authorized to access it, and what confidentiality and data protection requirements apply.
A particularly important issue is the protection of customer-specific know-how. The use of data, formulas, and models developed from them is defined in accordance with the agreed-upon project purpose and the contractual provisions. Any potential reuse of customer data for other applications or customers must be explicitly clarified.
We also take into account requirements related to hosting, access rights, IT security, and the integration of existing corporate systems when designing each solution.
Our goal: To enable you to take advantage of AI and data-driven optimization without losing control over your valuable business knowledge.
What are the costs associated with pilot projects, licenses, and ongoing operations?
The cost of an ai -predict solution depends on the specific task, the scope of the required software modules, and the individual requirements for integration and operation.
Getting started is free and without obligation. In an initial conversation, we will present our AI -core
-Platform, discuss your challenges and jointly evaluate the existing database as well as possible solutions.
For suitable problems, we can also conduct initial analyses or simple simulations within a framework agreed upon on a case-by-case basis to demonstrate the potential of an application.
For follow-up pilot projects, we prepare a transparent proposal that outlines specific services, objectives, success criteria, and costs. Whenever possible, we utilize existing software and model components to minimize development effort and investment risks.
The cost of subsequent implementation depends, among other things, on the required features, the number of users, possible licensing or usage models, and the requirements for hosting, integration, maintenance, and support.
Our goal is a financially sound entry into the market that delivers demonstrable added value. You can start by testing a specific use case and, based on that, decide whether and to what extent the solution should be expanded.
Our motto: Transparent pricing. Scalable in stages. No price surprises.
Can the software be expanded to include additional features, applications, and users?
Yes. The modular architecture of our ai -core platform is designed to grow with the needs of our customers.
Collaboration often begins with a specific question, such as optimizing a formula or simulating certain product properties.
Once successfully validated, such a solution can be expanded in stages, for example to include:
- Additional Properties and Target Values: In addition to color, other material properties, process parameters, or economic criteria can be taken into account.
- New models and specialized modules: Existing technology building blocks can be adapted, combined, and supplemented with newly developed functions.
- Additional Users and Departments: An application initially deployed in the laboratory can also be made available for use in development, quality assurance, or production, provided it is suitable for those purposes.
- Other data sources and systems: Additional measuring devices, databases, or existing software solutions can be integrated via appropriate interfaces.
- New Areas of Application: Proven solutions can serve as a foundation for further formulation and process challenges.
We will work with you to determine which enhancements are possible and what effort they will require, based on the technical requirements and existing components.
Our goal is to create a software foundation that can be used over the long term and adapted as needed, without having to start from scratch every time a new challenge arises.
In doing so, we rely on clearly defined development phases, transparent costs, and demonstrable economic benefits.
Any questions? Let’s talk about it.
Would you like to know how ai-predict can help you with your specific challenge?
During a free, no-obligation initial consultation, we’ll introduce you to our platform, discuss your requirements, and—if you’d like—take an initial look at your data and possible solutions.
Together, we’ll figure out what makes sense and is feasible for you.
Dr. Mathias Huck
Head of Marketing & Sales
Phone: +49 170 3281 473
Email: mathias.huck@ai-predict.de