Slush lightened Helsinki up once again during the November darkness. For those who have not visited Finland during this time of the year: don’t. Unless you’re coming to Slush.

Small and middle-sized companies need a low-barrier way to experiment with real data and real users without high barriers.

Matching is one of the most common, yet worst recognized, problems in both software engineering and data science fields.

There needs to be a tool that allows testing with datasets from various sources without a massive effort of normalisation and feature engineering. A tool that allows iterating the use case with real predictions, with real users.

In the following post, I will share my top learnings from leading the development of a SaaS platform called Aito.ai

Machine learning powered features, like recommendations and personalization, have become commonplace and as such the new normal. Customers have started to expect these smart features and the internal stakeholders and POs have started to demand them.

In our previous blog post, we introduced the new database category: predictive database. Now we’ll go deeper into how to use this predictive database.

How can Aito’s predictive database be used to forecast Freska's customer churn?

A lot has been written about the importance of Robotic Process Automation (RPA) and why it is necessary. And even more, how combining both AI and RPA is combining the best of two worlds.