Skip to main content



Gender Biases, our AI tool for tracking gender-coded language, allows you to measure and monitor inclusiveness and gender equality in the workplace. 


After collecting and analyzing over 225,000 job postings across all sectors, our solution identified a clear bias to female profiles in senior job offers, enabling the implementation of an action plan to increase diversity.


With which tools?

  • With the collection of hundreds of thousands of job offers

    After collecting and analyzing hundreds of thousands of job offers across all sectors, our solution identifies biases around offers based on seniority, sector, frontline proximity, etc.

  • With targeted analysis of your communications and job offers

    We analyze all of your communications, marketing elements and job offers to detect any bias that may be present and enable you to remedy it.

  • With follow-up action plans

    Implement targeted action plans to increase diversity and evaluate their effects over time.

Request a demo

Request a demo

*Required fields

This site is protected by reCAPTCHA and by the Google system.

Privacy Policy and Terms of Service apply.

Our publications


Boosting search engine capabilities of RegReview:…

RegReview is an AI solution for compliance teams, to automate regulatory monitoring and processes, which brings together several tools, the most essential of which is a search engine operating on a compiled database of custom-built regulatory sources.

The database contains ~300k documents.


Read more
Article title

Labeling text clusters with keywords

We propose to explore several keyword extraction techniques to label text clusters obtained after a Text Clustering or a Topic Modeling pipeline. This work is following our previous articles about Topic Modeling and Text Clustering (here and here).


Read more

Synthetic data or how to share sensitive data…

For a period of six months, 5 students from Centrale Supélec and ESSEC worked collaboratively with Sia Partners on building a Python library to create fake - which we'll call synthetic - data.
But what's the point of creating fake data? How could it help organizations?


Read more