Robot Emma (Electronic Multimedia Assistant) helps to improve marketing efforts.

A Python-based framework that pre-processes X-Road logs, prepares and forwards usage reports to members, displays anomalies based on moving averages, and anonymises and publishes log data as open data was developed. As a result of the project, the Information System Authority now has an overview of the institutions that use X-Road and how they use it. The system can provide guidance to users based on anomalies and meets the requirements for open data in the public sector.

The wild animal species recognition software is designed to determine the abundance of certain species using the REM method. The system allows for a project to be created for a specific survey area and duration, on the basis of which the abundance of species in the area is calculated. The system saves the images captured by trail cameras under the corresponding project, after which they are classified using AI and the REM method is applied to determine the abundance of species. The system enables the generation of reports based on the images collected under the project, following the structure defined in the terms of reference, along with the correction of observation cases with imprecise classifications and the training of the AI model using new images

The objective of the project was to explore the feasibility of using the use logs of digital learning materials to automatically generate models for assessing learning outcomes using machine learning techniques. These models enable the collection of more systematic information about the learners' progress in an automated manner, without disrupting their studies, and thereby provide input for better planning and automatic personalisation of further learning. As part of the project: 1) a visual prototype was developed to showcase the assessment results of learning outcomes; 2)
the initial models were trained using machine learning techniques and 3) a preliminary methodology for data science was established.

The tool detects prohibited advertisements in social media to reduce the workload of staff.

The Social Insurance Board collaborated with Feelingstream to carry out a pilot project for the detailed analysis of customer calls and online chats to identify patterns in customer enquiries. As part of the project, the Feelingstream application transcribed the calls made to the Social Insurance Board from May to August 2020 (three months) in both Estonian and Russian.

Extraction and structuring of information from electronic health records of Estonian residents through the use of machine learning technologies to ensure interpretable and high-quality data. The data has been transformed into a consistent and high-quality format suitable for research. This will allow researchers to effectively use the health data of Estonian residents.

The project had two main objectives: 1) to enhance the usability of the State Portal and 2) to create tools for the replication of analyses. Data analysis and process mining were used to identify the bottlenecks in the portal and to propose improvements, including prototypes. In addition, tools were developed to make it easier for product owners to decide which parts of the State Portal need most attention to enhance user experience and user feedback.

The Bürokratt personal data anonymisation application was completed in late 2022. The aim of the application is to make the training of Bürokratt enquiries even more secure. The data anonymiser is able to detect personal data name entities, such as names, personal identification codes and locations of persons, within a given text and replace them with another value of the same entity class (for example, Tallinn is replaced with the entity Tartu).
The application is designed to be used not only in Bürokratt but also in the applications and information systems of other authorities that are faced with the processing or storage of personal data.
The anonymiser helps mitigate the risk of personal data processing and complies with the regulations of the GDPR. The application enables the use of both anonymisation and pseudonymisation methods, depending on the user's needs. Integrated clients can use the application based on the NER corpora developed by the University of Tartu and further train the corpus with institution-specific data sets.

The first automated border control gates, or ABC gates, started operating at Tallinn Airport and the Narva road border crossing point in early 2021. Automated border control gates based on biometrics speed up border crossings and provide border guards with an additional tool for identifying people and verifying their right of entry. The identification algorithms used in ABC gates minimise the risk of people crossing the border with false documents. Furthermore, preliminary tests have shown that the average time required to pass through the ABC gates and cross the border is 15 seconds.

The process of keywording documents is a resource-intensive process. It relies on the knowledge and assessment of individuals and it is not always possible to guarantee the consistency of keywords across different versions of the same publication. The objective of the project was to develop a machine learning prototype to automate the content analysis and keywording of publications, support the development of Estonian language technologies, reduce costs, increase speed and ensure the objectivity and uniformity of keywords. A prototype was developed that is able to analyse the content of a publication and automatically generate keywords for it based on the full text.

Kiirkirjutaja is a solution based on speech recognition that automatically generates subtitles for TV programmes, which can be toggled on or off by the user according to their preference when watching TV.