Notre blog

Personalized virtual coach with Machine Learning

Imagine a fitness app where a virtual assistant helps users to reach their goals and to stay motivated. For the assistant to be engaging, it must give actionable and personalized advice, and adjust the plan to the actual progress of the user. In other words, a good coach must know its coachee.

Predictive UX boosts your website conversion rate

As many web-based marketplaces, banks rely on their website and mobile app to propose products like loans and insurances to the users. The conversion rate, that is to say the percentage of visitors that purchase a product, is often pretty low. The reasons are multiples: a great diversity of offers, non-targeted propositions, doubtful users… This article presents how Craft AI boost a website conversion rate by proposing adapted products to the users using predictive UX and hyper-personalized content.

Automated periodicity detection in time series

We recently presented a scientific paper during ECML PKDD Advanced Analytics and Learning on Temporal Data workshop part of the ECML-PKDD 2019 conference at Wurzburg, Germany.

How operationalizing AI improves online retailers

Online retail websites depend on their ability to understand their customer habits and wishes. These websites need to know which type of products interests which visitor and when they should make a suggestion.

How operationalizing XAI enables stock management?

Overstocks, out-of-stocks, dead stock … Retailers’ business depends on their ability to optimize their warehouse management. A lot of progress has been made in the last decade thanks to new management paradigms and control processes.

How eXplainable AI improves energy performance

Due to rising energy cost, scarcity of resources and the need to limit environmental impacts, Dalkia has decided to put the optimization of energy performance at the heart of its strategy. To meet this challenge, Dalkia created the Desc (Dalkia Energy Savings Center) back in 2013, a control center for energy performance management of its customers’ sites to ensure better comfort and lower energy consumption.

Information gain ratio correction

This paper presents an improvement of the information gain function used in a lot of decision tree Machine Learning algorithms. It was published on arXiv.org.

Periodic split method

This paper presents specific features of the Craft AI Machine Learning engine that enable it to better take into account typical rhythms in human activities. In particular, it improves the quality and explainability of predictive models related to those.

Forgetting Methods for White Box Learning

This paper presents some of the foundations of Craft AI and especially how we introduced Machine Learning of user habits in an explainable context. It also introduces the initial version of our forgetting method that is able to unlearn lost habits.