Machine-learning constitutes a decisive technological advance in the analysis of unstructured data in number, as can be for example the formulation data.

By applying artificial intelligence, it becomes easy to isolate, process and model the most relevant information and apply learning algorithms to fine-tune these processes without human intervention. As soon as the model is correctly configured, the machine learns on its own and over time optimizes the task at hand.

Augmented management transforms metadata. They are no longer used only for auditing, traceability and reporting, but they are starting to fuel dynamic systems. From "passive", metadata becomes "active" and feeds learning algorithms.

Quality, metadata management, Master Data Management, integration and even database administration, Artificial Intelligence is revolutionizing all facets of data management (Data Flow).

Thanks to machine learning, many manual tasks can be automated and the less technically qualified users can be more autonomous in their use of data, and highly skilled technical resources can concentrate fully on tasks with greater added value.

The process of finding a winning method or formula is complex and relies heavily on the expertise of the researcher, which by a trial-error-analysis cycle navigates by sight to find the right parameters. On new raw materials that are poorly mastered, the research can sometimes be tedious.

Design Flow, the 1st artificial intelligence-based assistant for formulators, helps build an intelligent pipeline that optimizes prospection of the formulatory space.

The growing power of analytical means allows, for example, the formulator to go further and further in understanding the different properties of mixtures. This is one of the strengths among other use cases, such as :

1- Find the composition in the formula for each raw material
Taking into account the constraints of toxicology, cost, stability, harmlessness
2- Replace a process or a material following a shortage or a regulation
3- Understanding the synergies within a formula
4- Understand the impact of each component in the formula
5- Evaluate the impact of a parameter in a process
6- Virtualize the formulation to have the information in real time
7- Predict the characteristics of a formula never realized
predict long characteristics to measure to limit the risk of Go / NoGo

The implementation of artificial intelligence often requires expert data scientists, computer scientists and a significant investment in human resources, without forgetting an often long implementation period, varying from 1 to 2 years.

The rise of AI is also accompanied by the development of explainable AI (EAI). In other words, the results of a treatment based on Machine Learning and Deep Learning are now rendered in an ergonomic and fluid way.

Thanks to its Lab and the application portfolio made available, InFlows makes it possible to quickly adapt the specific applications of the company, for the analysis and processing of the formulation data flow.