Contextual data to support your transformations


Harness our contextual data to optimise your projects in areas as varied as the ramp-up of energy renovation, the roll-out of renewable energies, the fight against artificialisation, insurance and banking risk or, more generally, the uses and qualification of the built environment and regions.


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“Our off-the-shelf data allows you to enhance your client or property databases.”

Simplify the complex.

Our strength, a unique technology in artificial intelligence to provide you rapidly with the data that interests you.

Simply access contextual, original and unique data
from an address, a municipality or a zone.

All the databases are linked to massively increase their value
by design
No qualifier relates to individuals and the primary sources are public
Each item of data has its own confidence index resulting from its production process
The database entry point is via an address, a zone, an IRIS, a municipality, etc.
The qualifiers are complete for the whole country and predicted if initially partial
The qualifiers are enhanced by multiple data sources
The addresses, zones and municipalities are all entries in the database

Use our data to better manage your clients and assets

Qualify your assets or incoming leads
Learn about all the attributes of an address or zone
  • Localised API call
  • 10 atrributes
Contact us
Target your best opportunities
Filter by zone according to your criteria
  • 5 qualifiers filtered
  • 10 atrributes
Contact us
Optimise the monitoring of your assets and clients
Learn about all the attributes of an address or zone
  • Localised API call
  • 10 atrributes
  • + Future qualifiers
  • Monitoring over time
Contact us

Client references

“It [the tRees program] is presented as a real lever for action in favour of the energy transition and the platform entered the testing phase in the Hauts-de-France region from the start of the 2020 school year. It uses complex machine learning models (including computer vision) to recognise millions of actionable, filterable and organisable attributes.”

Thomas Calvi pour ActuIA

“By adding numerous other parameters, such as the number of floors of a building, the presence of an air-conditioning unit on the roof, the material, shape or slope of the roof, and by drawing on its Data Library, nam.R was then able to optimise the choice of the location of solar infrastructures, at the level of either a building or a district.”

Bothorel report, Pour une Politique Publique de la Donnée, December 2020