Skip to main content

Geospatial data provides crucial information to determine the ideal location for your future operations. AI can process these huge volumes of data to fuel decision-making about your future facilities.

Functionalities

    Supply/demand ratio and constraints management

    Using geospatial data, AI determines supply and demand scores based on rules you define.

    ""
    Add constraints to refine the optimal configuration of your development projects. Use unlimited data sources and volumes to make objective decisions.
  • Location of zones and exploitation of data sources

    Identify the most suitable areas for your development projects in real-time from multiple data sources. Reduce the search area for potential sites.

    Screen SD 2
    Satellite images, information on electrical networks, gas networks, road traffic, parking areas, etc.
  • Use your feedback

    Screen SD 3
    Improve Smart Deployment performance by integrating corrected and updated data.

SMART DEPLOYMENT IN ACTION

  • ELECTRIC CHARGING STATIONS NETWORK

    Smart Deployment has helped optimize the implementation of electric vehicle charging devices.

    The company went from 7,400 potential locations to about 250 target locations in Belgium (-96.6%) and from 8,726 to 250 in Quebec City (-97.1%).

    The reduction in manual work allows the teams to focus on higher value-added tasks!

  • CONSTRUCTION

    One of our construction customers uses satellite imagery and Smart Deployment's overlay visualization module (traffic networks, parking spaces, electrical networks, gas pipes, etc.).

    This allows him to analyze the environment in which it operates and to explore the data at an extremely precise scale to optimize its activity.

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

image_background

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.

2024

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).

2024

Read more
image_eng

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?

2024

Read more