Skip to main content

The average frequency with which companies move is 3 to 5 years. Take advantage of this strategic event! We feed your sales teams qualified leads by calculating the probability of your prospects moving.


    AI for sales targeting

    You define your prospecting perimeter to analyze and follow your target prospects.

    You define your prospecting perimeter: 
    • Geographical,
    • Activity sector,
    • Size,
    • Seniority etc.
  • Anticipate and target

    Our solution provides all the relevant information on your targeted companies in order to optimize your commercial approach

    Screen CMP 2
    • Probability of relocation
    • Financial and economic performance
    • Sector of activity
    • Workforce
    • Managers
    • Seniority of the establishment
    • History of SIRETs
    • Contact details of prospects etc.

What our customers appreciate:

  • Logo 1

    Pre-qualified leads

    Pre-qualified leads with 2x the chance of moving in the short term

  • Logo 2

    Improved results

    A 95%* increase in sales

    ( *Average figure observed with our customers )

  • Logo 3

    Improved efficiency

    Division of time dedicated to prospecting by 5*.

    ( *Average figure observed with our customers )

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