A distinctive feature of the iML system is the work of artificial intelligence.
The system processes and analyses user behavior, their preferences, habits, and based on this data it offers recommendations aimed at a comprehensive life improvement.
The system is self-learning and offers personalized tips for each user. Recommendations are not hardwired into the system, they are formed personally for each user.
Work on improving the recommendations is ongoing and does not require updating the app.
A user gives the Health category a low score, so the system suggests getting a gym membership and making going to the gym a habit.
A user completes the majority of their tasks around the same time of the day, which means it is their most productive part of the day. So the reminders for future tasks will pop-up around this time.
Or, a user gives the Finance category a low score and the system suggests defining a key task to influence this segment, as well as creating a recurring evening task «Accounting of the Daily Expenses».
A user gives some category a high score, while the majority of the tasks are concentrated in this category. The system suggests focusing more on the lacking segments, adding key tasks and dedicating more time to them, while delegating the tasks in a better-performing category.
Please note that the system does not require any emails, texts or other personal data of yours to work.