Our expertise in Data Strategy, Machine Learning, Chemistry, Formulation, UI-UX Design, IT...
InFlows AI, thanks to its turnkey application portfolio, provides you with digital assistants easily deployable within your IS to assist formulation laboratories and production units. Our responsiveness allows us to quickly provide consultants, multidisciplinary project teams to implement specific applications or assist you in the creation of digital solutions.

« Where AI meets process engineering and Chemistry » ... you will find InFLOWS AI (Intelligent Process Flows).
InFLOWS AI was born out of a desire to promote actionable data strategy to drive more efficient, competitive and responsible manufacturing.
Equip scientists, accelerate time to product, predictive analytics to anticipate risks, improve collaboration across teams, strengthen operations traceability...

-70% reduction in your development timeframe

InFLOWS AI allows you to capitalize on the capabilities of artificial intelligence and machine learning to boost each step of the formulation process.

InFLOWS : Intelligent Process Flows

The cosmetic, food and nutrition, pharmaceutical, chemical and materials industries face recurring tensions during the product design phases.
The challenge: innovate / design high-performance, competitive, stable, eco-sustainable products, on short notice: Time to Product.
In addition, there are aspects such as process traceability, collaboration management, knowledge historization, control of R&D costs ... As examples, we can cite the integration of a new innovative active in a formula, the development of a food recipe by minimizing the rate preservatives, the replacement of a raw material subject to regulations, the implementation of an industrial process with less energy footprint ...

Technicians, formulators, engineers, scientists strive to push the boundaries of innovation by experimenting with processes, settings and mixtures of materials. The intuitive approach consists of starting with a "first guess", i.e. a process / formula approaching the target, then by a set of increments progressive inputs, to navigate visually to try to achieve the expected performance. This process is tedious, because for a formula comprising 10 parameters each taking 10 values, there are 10 billion combinations. Which is prohibitive for physical experiments.
In general, the expertise, the operational constraints, the business know-how make it possible to avoid certain improbable combinations for reduce the space of possibilities to a few hundred or even thousands of configurations. But it remains high all the same. In addition, since the exploration is not planned, it becomes difficult to learn the lessons of the causal relationships. within the process, to capitalize on the tests carried out. This can lead to redoing the same experiences for a slightly different need.

Mathematical models can be used through digital simulation and therefore virtualization of experiments. However, this assumes that the underlying model linking inputs to outputs is known, requiring years of research. to establish and implement it. This model lapses as soon as a new component is added or removed. Which is not possible for an industry that wants to be agile.

Statisticians invented the methodology of experimental designs (Fisher 1935). In summary, it consists of planning a limited set of experiments intended to crisscross the field of research, then to establish with these experiments a simplified model of the relation of cause and effect (linear, quadratic, first order interactions…). The inference on the objective is made by exploiting the coefficients of the regressions, and predictions. This methodology has shown its effectiveness in various fields in the past. However, there are 2 drawbacks to remember:
- you must have a statistical background to implement them.
- the current complexity of formulas and procedures can no longer be explained simply with the over-simplified assumptions of classical experimental designs.
In chemistry, for example, we will have phase transitions, breaks in phenomenon ... A daily example is evolution the temperature of a block of ice subjected to a heat source. Temperature levels are observed during changes of state, and almost linear variations, for the homogeneous phases, giving way to a stepped model.

It is to these challenges that InFLOWS offers an answer with alternatives based on intelligence artificial in particular Machine Learning). Machine Learning is a branch of artificial intelligence that is based on approaches statistics to give computers the ability to "learn" from examples. In particular, to model relations without having to explicitly specify or program them. Very well, this is what we are trying to model the behavior of substances without an explicit postulate a priori on their synergies and then generalize the hidden relationship between the inputs and performance of a product.
An example of a model: make a program that knows how to differentiate an apple from a pear.
We can see that this is an easy exercise for a child, but more difficult to specify explicit rules for a computer program.
For this example, Machine Learning will consist in presenting to a computer program inspired by the structure of the brain, successively several examples of apple and telling him this is an apple and several examples of pear. At the end of this automated learning, the program will be able to extract by itself the hidden characteristics (eg: invariants) that make an apple an apple and vice versa. The program will then be able for a new image to say which fruit it corresponds to, this is the faculty of generalization. You will understand, this example is greatly simplified, the process of architecture and learning of Machine Learning methods is much more complex and is one of the research topics of greatest interest. Their implementation requires data science experts (Data Scientist), to implement the analysis pipeline either on fresh data or for retro-analysis on historical data. These profiles are not always available, and can represent a point of contention.
To meet these challenges, InFLOWS has surrounded itself with a multidisciplinary team to cover the entire development cycle digital assistants to give back autonomy to scientists (physicist, chemist, computer scientist, developer, administrators, ergonomists, UI-UX, data engineer, data scientist, Agile project manager etc.)

InFLOWS provides a catalog of turnkey applications to boost process engineering. The ergonomics, interfaces, onboard calculation engines are the result of more than 10 years of experience by machine learning experts. The procedures are automated so as to reproduce the steps of the data scientist workflow, and to provide feedback interfaces and visualization for researchers. Our assistants are deployed in the internal IS of our customers with a historization on-premise database experiments. The projects carried out are digitized and their results can be shared within the company offering the possibility of collaborating with distant colleagues. Monitoring interfaces make it possible to monitor project KPIs, and application administration. We also offer the development of customized applications for the needs of our customers. Our working methods are largely inspired by Agile methodologies such as Design Sprint, or how to prototype in 5 days.


The values our action is based upon :


This is the founding value of InFLOWS. Observe, Listen, Design, Test, Improve. Our work processes are highly inspired by the Design Sprint, or how to create a prototype in 5 days.


Passionate players in the implementation of Artificial Intelligence for more than 10 years, we see it as an inexhaustible source of innovation for the engineering, physics and chemistry of tomorrow.

Business expertise

Physics, Chemistry, expertise in data valuation strategies, design and ergonomics, this transversality allows us to design solutions very suited to process engineering.


The challenge of our efforts is to assist technicians, engineers, researchers with software, equipped with simple, ergonomic interfaces, restitution, visualization and interaction.


Engage with our clients on results and support over time, resolve specific issues and guarantee their satisfaction.

The collaborative spirit

Assist and not replace. We are working to ensure that the contribution of AI within the assisted processes is an accelerator and a federator within the company, not a replacement tool.


Imagine tomorrow's engineering, we want to give you the means of a fast and simple implementation.

Abdou KANE

• +12 years background in AI, digital and IT
• Former Data Science Manager for Labs at L’Oréal R&I. Award-winning AI-driven projects
• IT and architect at ErDF, SG and BNP banks
• PhD in Applied Mathematics at the French Atomic Agency. Master’s degree in Materials Physics
Contact him by email : Akane@InFlows.io

Olivier CHATIN

• +30 years consulting and management
• Former COO BearingPoint Group & France CEO
• Board member of private companies
• Graduated from HEC – CPA - IFA/Sc Po
Contact him by email : Ochatin@InFlows.io
© 2021 InFLOWS AI : Intelligent Process FLOWS