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

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.

A predictive database lets you query predictions from structured data with no training step — classifications, recommendations, and missing values — using the same query interface you use for data retrieval.

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.

Machine Learning changes the design of intelligent conversational bots. In order to provide the same capabilities without using ML, an application would have to ask the user to answer a long set of questions, which would negatively impact the user experience.

Aito, a Helsinki-based startup that has built a predictive database for developers, has raised $1.1 million in pre-seed funding.

Startup life. The lure of setting your own schedules and priorities. Being your own boss. Being able to focus, if not on one thing, then at least on one dimension of things.