Transformation powered by AI

What we do

Disaitek is building cutting edge technologies on natural language processing to help enterprises leverage their conversational flows and make their transformation faster.

Regarding AI, Disaitek area of expertise is the use of deep learning methods for NLP tasks. More specifically, we use neural networks that have been trained in an unsupervised way on a huge corpus of documents and transfer their knowledge to other problems. In order to do this we use state of the art semi-supervised learning algorithms and create proxy tasks to get the most value possible out of our data.

Disaitek has been contributing to implementation in PyTorch of GPT and BERT, large pre trained model that capture deep information about syntax and grammar of languages.

Disaitek has been labelled as a Young Innovative Entreprise (Jeune Entreprise Innovante) by the French administration. It’s a recognition of our R&D program and of the innovation that we want to bring to market.

What we propose so far

Transformation is a nightmare for most C-Level. Budgets that are allocated to IT and other services do not drive to the expected performances. Huge amount of money is wasted which can lead to dramatic outcomes for enterprises. Among the long list of root causes, we are tackling two majors ones.

Part of the problem is that we still rely on human on most non cognitive processes. At Disaitek we are building a solution to help people focus on their cognitive tasks. And we do it along a fundamental objective: reduce the amount of workflow changes needed to use our application. We do not want to add more transformation on transformation.

Task management

Leverage AI to collect and structure tasks on the behalf of humans
How do you better engage people to execute your transformation?

Commitment is not fully connected yet, there is a lack of visibility on who is engaged on what and what is a correct evaluation of the overall status of projects.

Learn more…

Intelligent Knowledge Capture

Automatic collection of knowledge. Automatically update existing knowledge.
How do you better capture enterprise information and ease access to it?

Knowledge is still not correctly addressed in enterprise and remains a major problem.

Learn more…


LEFEBVRE SARRUT – 2nd largest European legal publisher

Disaitek has signed a strong partnership with Lefebvre Sarrut

This partnership allowed us to develop and train our deep learning models on a huge volume of real data produced in 5 different languages (English, French, Dutch, Spanish and Italian). Beyond that, ELS hosted Disaitek prototype in order to collect valuable feedback from end users.

ENSIIE, Associated member Paris Saclay

Disaitek announced the signing of a major research partnership with @ENSIEE, an engineer school associated to Paris Saclay. This agreement, one of the very first of this kind in France, will focus on research of vulnerabilities of machine learning systems to malicious attacks.

Founders / Owners


Founder, CEO

Master of Civil Engineering

20 years of experience in consulting and project management.
Previously founded a Fintech which was merged with an ETI as for exit.
Concerned about compliance and regulation, he was also chairman of the supervisory board of a financial institution under FCA regulation.
Passionate about artificial intelligence and computational neuroscience. Anthony passed five certifications with Stanford university and university of Washington.
He’s in charge of the product roadmap and business development.

Gregory CHATEL

Associate, CSO

PhD Comp. Science

Passionate about artificial intelligence, he wrote several blog posts to explain how deep learning works and build awareness around security issues.
He is a member of Intel Innovator program and was asked to talk in 3 meetups in Europe to present privacy and security concerns on machine learning.
He is also specialized in NLP and is an active contributor to multiple open-source projects such as popular deep learning libraries and implementation of research articles.
He is in charge of research & development on Deep Learning stack.

We’re hiring !

PhD Computer Sciences, Mathematics or Physics

Doing research on unsolved NLP tasks for now. Especially around abstractive summarization

Data Scientists

We want to leverage continuous learning from users to optimize our deep learning stask and to personalize the way the systems act regarding the user habits


We want to build great experiences for our users and let them feel comfortable with our solutions. Great user experience for great people (technology .js)


Let’s Meet UP !

Disaitek has developed strong expertise while driving R&D works….We propose to share it !

Meet UP has become a nice way to gather people and expert pleased to explain and share insights on lots of discipline. Traditionally this meeting takes place in a remote place, gathering people from different horizons (employees, independent, founders, VC, academics…). Usually these people act outside of their work hour and independently from their company or others.

We think company must leverage this kind of activity and bring it inside your company

Why ?

It creates condition to innovation: Giving new insights to employees, they can imagine applicable solution on their work and in the process they are involved in

Disaitek offers to bring valuable meet up inside your company. Depending on the level of the attendants you program to invite, several agendas are possible.

If you are interested in bringing information to your employees on breakthrough technology, if you want to experiment new engagement forms with them, contact us. You just have to provide snacks and refreshment and we handle the presentation for free!

Meetups can be done either in English or French.

Introduction to fundamentals of Machine Learning

Ok, what’s really behind the scene?

Who ?

All kind of employees curious about this phenomenon and who try to find new tools to increase productivity in their work.


  • Give better intuition about what covers Machine learning
  • Categories of machine learning
  • How to train a machine learning model
  • When it comes to evaluate the precision of your model
  • Relevant frameworks of machine learning and AutoML
  • Bias in machine learning
  • Machine learning for NLP
  • Recognize context in your day to day job where machine learning can apply


Help to come up with relevant use cases suitable for your company

Unlock the potential of your Data

Hard to have enough labelled data?

Who ?

Data scientist, people involved in machine learning development and concerned by the low quantity of labeled data.


  • How to train deep learning models on language modeling helps capturing syntax and semantic of languages
  • How to use to transfer this knowledge to other tasks
  • Multi-task learning: principles
  • How multi-task learning help models generalize better
  • Principles of semi supervised learning
  • Example of Virtual Adversarial Training to reduce the need of labeled data


New ideas to tackle existing problems

Machine Learning privacy and security

How really secure and ensure your models respect customer privacy?

Who ?

All people involved in machine learning development


  • Key dates for this discipline
  • Principles of security and privacy of machine learning
  • State of the art in machine learning security and privacy
  • Backdooring
  • Poisonning
  • Model stealing
  • Data stealing
  • Principles of prevention. security and privacy by design
  • Watermarking
  • Differential privacy


First evaluation of the risks of currently deployed models or systems