How it works

You want to take advantage of the benefits of artificial intelligence but you don’t know how and by which project to start? Want to know how to turn your data into value for your customers and employees?

Both intuitive and powerful, the Datakeen solution is the IA Enterprise platform designed for teams who want to fully leverage their data assets without committing to long and costly projects. It is also suitable for data departments that wanted to accelerate their processes.

The platform, the simplest on the market, will allow your employees to centralize their data and collaborate on high value-added projects. Datakeen’s central collaboration point has packaged algorithms to save time in your AI projects. With a few clicks, perform self-service analyses and deploy powerful predictive models. Private by design, the platform will also help you comply with the DGMP.

The platform consists of 3 main layers:


The first layer of the platform is a Datalake (centralized file system) of the Amazon S3 type that allows you to centralize all the data from your different systems and tools.

Production, marketing, sales data: feed Datakeen with all types of files. It is possible to import text data, tables, images or video and audio recordings. It is possible to load data from your workstation or to connect existing databases and SaaS tools to it.


The various data analysis projects are collaborative: a sharing system and a chat is available for collaborators.

A system of roles and permissions controls the access and features that belong to different collaborators.

A precise and nominative history of the transformations that have been carried out on the data and available through the interface.



Once the data has been aggregated and centralized, it is possible to implement advanced AI techniques for, among other things:

  • finely segment customers for marketing campaigns
  • identify and prevent breakdowns or failures in production processes
  • classify documents or emails automatically to save operational time

How to approach these different projects in a standard and unified way with the state of the art in terms of automatic learning method (Machine Learning)?

How can these use cases be brought into production with an end-to-end pipeline tested, monitored and transparent once they have been experienced?

Datakeen, based on container technology (Docker), has a resource orchestrator that allows it to move from experimentation to production with complete fluidity. The models are also optimized and monitored over time to ensure a high level of quality and incremental improvement over time.



Interested? Let’s discuss your issues and ask for a demo.

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