Case
studies

Value-based healthcare is a healthcare delivery model in which providers, including hospitals and physicians, are paid based on patient health outcomes. ​It differs from a fee-for-service, in which providers are paid based on the amount of healthcare services they deliver. ​The “value” in value-based healthcare is derived from measuring health outcomes against the cost of delivering the outcomes. ​

Under value-based care agreements, providers are rewarded for helping patients improve their health, reduce the effects and incidence of chronic disease, and live healthier lives in an evidence-based way.​ To make it happen, new healthcare data infrastructure must be built and smart trustworthy algorithms must be developed to estimate the provided value and potential gaps.

Tailored digital prevention (Following the latest trends in public health (Prevention is Better than Cure1): We introduce predictive prevention programmes for selected diseases.

We will create a population health risk model and enable the development of pesonalized prevention programs tailored to at-risk patient groups (especially for obesity, diabetes, and hypertension).

There is a need to significantly increase the energy efficiency of transport, solve the parking problem, better utilize the various means of transport, and change the look and feel of cities. New ways of transportation can have a huge and positive impact on urban planning. Car manufacturers will need to move from a business model of selling hundreds of thousands of cars to individual customers to a model of operating fleets of autonomous vehicles and providing comprehensive transport solutions and services.

Ensuring high-resolution maps with many data layers will be the key to providing new integrated and automated transportation services for people and goods. Therefore, it is essential to set up a business model and design the infrastructure and methodologies for collecting and using the map data necessary to deploy autonomous mobility and increase traffic fluency. Vehicles and other devices equipped with sensing systems will collect this data through a licensed and regulated infrastructure communication network.

Economy must be regulated by regulations that work and are based on evidence. New technologies such as semantic analysis of legislation, artificial intelligence, and real-time data analytics will enable the systematic improvement of a regulatory framework containing many regulations.

Therefore, we want to support the assessment of regulatory impacts with a comprehensive IT solution capable of processing large amounts of relevant data to monitor regulations' implications. This platform could provide the main stakeholders in the decision-making process with sufficient tools to assess the calculated impacts and collaborate with each other to improve the regulatory environment.

Alistiq envisions public services that anticipate citizen needs rather than merely respond to them. Building on our work shaping Slovakia's Digital Transformation Strategy 2030 and the National Concept for Public Administration Digitalization, we see the next frontier as creating truly proactive government—where AI-powered systems identify life events (childbirth, job loss, retirement) and automatically connect citizens with relevant services before they even have to ask. Our goal is to help governments achieve 100% of priority services online while automating up to 40% of deterministic administrative processes, fundamentally shifting the relationship between citizens and the state from bureaucratic transactions to seamless support.

Central to this vision is our commitment to better regulation and evidence-based policy-making. Through platforms like the IT system for Regulatory Impact Assessment we designed for Slovakia's Ministry of Economy, we aim to transform how governments create, evaluate, and refine regulations. The future we're building includes AI-assisted regulatory analysis that identifies contradictions and administrative burdens across thousands of laws, predictive impact modeling, and continuous monitoring of whether policies achieve their intended outcomes. As governments worldwide move from AI pilots to operational deployment in 2026, Alistiq is positioned to guide this transition—ensuring that intelligent automation serves citizens while maintaining the transparency and accountability that democratic governance demands.

Alistiq believes social protection must evolve from reactive benefit distribution to predictive, personalized support that reaches people at the moment of need. Our work with Slovakia's Social Insurance Agency—building simulation models that optimize branch networks and predict citizen demand across 88 locations—represents the foundation for a broader transformation. We envision social services infrastructure that uses AI to anticipate unemployment risks, identify citizens eligible for benefits they don't know about, and streamline the journey from application to assistance. The goal is not merely efficiency but dignity: reducing wait times, eliminating redundant paperwork, and ensuring that vulnerable populations receive support without navigating bureaucratic labyrinths.

This transformation must be grounded in ethical AI principles and citizen data sovereignty. Unlike automated welfare systems that have deepened inequality in some European countries through opaque algorithms and surveillance-based approaches, Alistiq champions a human-centered model where AI serves as an enabler, not a gatekeeper. Our Institute of Dataism and commitment to MyData principles mean we develop social service solutions where citizens maintain control over their personal information while benefiting from AI assistants that can guide them through life transitions—whether that's navigating parental leave, managing a chronic illness, or planning for retirement. As the OECD recognizes, AI has transformative potential for social protection, but only when implemented with robust governance frameworks that we help our clients design and deploy.

Alistiq sees official statistics transforming from periodic snapshots into living, near-real-time intelligence systems that inform better decisions at every level of society. Our pioneering work with the Statistical Office of the Slovak Republic on flash GDP estimates—using toll system data to predict economic performance weeks ahead of traditional methods with 0.67% accuracy—demonstrates what becomes possible when unconventional data sources meet rigorous statistical methodology. We envision national statistical offices equipped with AI-powered pipelines that integrate satellite imagery, mobile network signals, transaction data, and IoT sensors to deliver economic and social indicators at unprecedented speed and granularity, enabling governments and businesses to respond to changing conditions as they happen rather than months after the fact.

This modernization must preserve the core mission of official statistics: providing trusted, impartial data that citizens and policymakers can rely on. As Eurostat and national statistical offices across Europe embrace alternative data sources and big data techniques, Alistiq brings the methodological expertise to ensure quality, comparability, and privacy protection. Our research on mobile network population dynamics with Comenius University established frameworks for extracting insights from telecom data while maintaining strict anonymization standards. Looking ahead, we see opportunities to apply machine learning to automate classification, improve sampling, detect anomalies, and generate experimental statistics that complement traditional surveys. The future of official statistics is hybrid—combining administrative records, sensor data, and targeted surveys through AI orchestration—and Alistiq is helping statistical offices navigate this transition while maintaining public trust.