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Democratizing the access to leverage ML

Everyone is talking about AI and ML. Optimizing this, automating that. However, 87% of these initiatives never reach production.

Most often, it's not the models itself that is the roadblock. Rather all the work with the data that is needed. Meaning, have it collected, prepared and ready. Which in many cases, takes 80% of a data scientists time.

To solve this, and to have data running smoothly, a full team is needed. Making it a very resource-heavy operation with slow iterations. Leaving a lot of companies behind to leverage ML. 

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With, the production time was cut by 45% to create the foundation of our dynamic pricing model. We now have continuous clean data, that is auto transformed, in a stable pipeline. Allowing us to create models way faster. 

Jaime, Co-founder / CTO

With, we have been able to collect and combine external data to have it ready for modelling. Allowing us to create recommendation models with ease with no need to hire data engineers. Enable us to increase revenue by 15%

Alan, Co-founder / CEO

An easy to use data pipeline and preparation tool, with intelligence.

Removing the data bottlenecks for ML models and decisions, by enhancing the data scientist

  • Webscraping

    Gather your data from the web sources and automate the use of your computer with RPA

    In some data tasks it happens, one would like to add some data that need to be extracted from external web sources or some old legacy apps which don't have API. For that reason, comes with a set of Webscraping and RPA (Robotic Process Automation) tools to ensure you don't have to switch to other tools to gather the data you need for your work. 

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    Set up, orchestrate and manage ML workflows at scale helps you build, deploy and manage data pipelines for your ML models - in minutes instead of weeks. No data engineers needed. A graphical UI to oversee the pipeline, with code interface for version control and easier editing.

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    Sweep through the data prep

    Data preparation, with AI-assistance. By inserting data, it learns from its structures, the user actions and the user high level objectives (e.g for the training of a dynamic pricing model). To overtime, automate more and more of the mundane data tasks, ensure data quality and enhance the data scientist.

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    Proactive quality assurance

    Ever had a new batch of data coming in that is formatted differently and make things crash? proactively monitors the quality of incoming data. If it deviates from the threshold - you get alerted. 

Now launching beta

Geared towards startups and scaleups to create state-of-the-art data workflows for ML-products without needing to spend a fortune. Love to have you on board. 

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