Would you like to reap the benefits of artificial intelligence, but don't know how or where to start? Would you like to know how to turn your data into value for your customers and employees?
Both intuitive and powerful, Datakeen is the AI Enterprise platform for teams wishing to make the most of their data assets, without committing to costly, time-consuming projects. It is also ideal for data departments wishing to accelerate their processes.
The simplest platform on the market will enable your employees to centralize their data and collaborate on high value-added projects. Datakeen is the central point of collaboration , with fully packaged algorithms to save you time in your AI projects. In just a few clicks, perform self-service analyses and deploy powerful predictive models. Private by design, the platform will also help you comply with the RGPD.
The platform comprises 3 main layers:
AGREE ALL YOUR DATA
The first layer of the platform is an Amazon S3-type Datalake (centralized file system), enabling you to centralize all the data from your various systems and tools.
Production, marketing and sales data: feed Datakeen with files of all types. You can import textual data, tables, images, video and audio recordings. Data can be loaded from your own workstation, or connected to existing databases and SaaS tools.
COLLABORATE ON YOUR DATA
The various data analysis projects are collaborative: a sharing system and a chat room are available for collaborators.
A system of roles and permissions allows you to control the accesses and functionalities that belong to different employees.
A precise, nominative history of data transformations is available through the interface.
from experimentation to production
Once the data has been aggregated and centralized it is possible to implement advanced AI techniques to, among other things:
- finely segment customers for marketing campaigns
- identify and prevent breakdowns or faults in production processes
- automatically classify documents or emails to save operational time
How can we approach these different projects in a standard, unified way with the state of the art in terms ofMachine Learning methods?
Once these use cases have been tried and tested, how can they be put into production with an end-to-end pipeline that is tested, monitored and transparent?
Datakeen, based on container technology(Docker), features a resource orchestrator that enables smooth transition from experimentation to production. Models are also optimized and monitored over time, guaranteeing a high level of quality and incremental improvement over time.
Interested? Let's discuss your issues and request a demo.