Health intelligence for the public good, built and governed in Europe
Privacy-preserving analytics and causal inference infrastructure for European health data. We hold no data. We broker no access. We make granted access computable.
Privacy-preserving analytics and causal inference infrastructure for European health data. We hold no data. We broker no access. We make granted access computable.
Federated causal learning and causal inference infrastructure for European health data, deployed on EU sovereign cloud. Computation moves to where the data lives, the data never moves.
We hold no health data, broker no access, and accumulate nothing. The computation moves to where the data lives — sovereignty stays with the institution that owns it.
Our architecture is modular by design. Each layer delivers independent value and can be deployed without the others, because in sensitive industries, infrastructure that demands total adoption before delivering any value does not get adopted at all.
The base layer: privacy-preserving federated learning infrastructure that keeps data sovereign. Models train across distributed sites without centralising records. Only encrypted model weights travel, never patient data. Built on EU sovereign cloud to eliminate supply-chain dependency on non-European hyperscalers, ensuring every component passes audit under the EU AI Act.
Federated causal learning, not just federated training. This layer builds directed acyclic graphs and applies targeted maximum likelihood estimation across sites to identify what actually drives health outcomes. The distinction matters: correlation at population scale is not prevention intelligence. Causality is.
Not all determinants of health are indexed in clinical data. Environmental exposures, occupational conditions, socioeconomic factors, proximity to environmental pollutants, and cultural access barriers to care, these shape health outcomes as decisively as any biomarker. This layer integrates those determinants into the causal model, with differential privacy preserving the sensitivity of contextual data.
In regulated industries, what cannot be audited cannot be deployed. This layer ensures that every inference, every data access decision, and every model output is humanly auditable and verifiable, not merely humanly readable. Compliance by design, not compliance by retrofit. This is the layer that enables adoption, because it is the layer that enables trust.
The market has federated learning frameworks and health data platforms. None combine privacy-preserving federation with causal inference for chronic disease prevention, and none do it without taking custody of the data.
Federated training alone produces predictions. Federated causal learning produces explanations of which interventions change which outcomes, for which populations and health systems, under which conditions. Prevention requires understanding mechanisms, not just patterns.
Purpose-built for chronic disease prevention and population health. The exposome layer integrates environmental, occupational, and socioeconomic determinants at scale, targeting the upstream: what makes people sick, and what can be changed before they are.
Fairness-aware models that account for socioeconomic proxies and structural determinants are not a feature; they are foundational. Health intelligence that systematically underserves all social populations is not intelligence. Equity constraints are built into the model architecture, not applied as an audit layer after the fact.
Sovereign technology on EU cloud infrastructure, eliminating supply-chain dependencies that would fail regulatory audit. Every layer is built to meet the EU AI Act, GDPR Article 9, and EHDS secondary-use requirements from the first line of code, not retrofitted to pass inspection.
Health data exists. Legal access is expanding. But without sovereign, privacy-preserving analytics infrastructure, that access produces no insight, no prevention, and no value.
Not because data is absent but because nothing connects fragmented records across institutions and borders into actionable prevention intelligence. Chronic disease catches people too late, when the evidence to intervene earlier already exists, distributed across systems that do not communicate.
Insurers and public health agencies require population-level analytics that operate across institutions without moving sensitive data. Every past initiative, digitisation programmes, data-sharing pilots, interoperability projects, created components. None created a usable, compliant analytics layer.
Population-scale health data could transform drug development and post-market surveillance. But without sovereign federated computation, that data remains inaccessible to the research enterprise and the insights it could yield remain unrealised.
Large employers require health-adjacent analytics for absence management, occupational risk, and preventive programmes. The current choice is between tools that fail regulatory requirements and no tools at all. Pre-vetted, sovereign infrastructure changes that calculus.
Loretta co-develops with the actors shaping the European health data space, from the regulatory bodies writing the rules to the enterprise partners deploying infrastructure in the field. We are not trying to be disruptive. We are trying to build. That means building on top of what exists, setting standards together with the system, and operating as stewards of a public good.
Healthcare is tedious. It requires painstaking attention to detail, incremental progress, and the discipline to resist silver-bullet thinking. There is no shortcut to trust in this system. We earn it by building correctly, transparently, and in service of the institutions we work within.
Designing infrastructure alongside the EHDS, the EU AI Act, and the GDNG, not retrofitting after publication. Our compliance posture is shaped by direct engagement with the policy environment, not by interpretation at a distance.
Collaborating with leading European research cohorts and academic partners to validate causal models, refine federated learning protocols, and advance the science of privacy-preserving population health analytics.
Embedded in industry-wide efforts to harmonise the European health data landscape. Working alongside pharma, medical technology, and health system actors to define standards, prototype infrastructure, and build the shared foundations the system requires.
Partnering with system integrators and enterprise clients to deploy modular infrastructure in real operational settings, from insurer analytics to workforce health programmes to hospital network optimisation.
We define infrastructure as the jobs to be done, the evidence that needs to be generated, the applicability of that evidence in other settings, and the diffusion of technology and practices. This is what our platform produces.
Personalised chronic disease risk models powered by federated causal learning. Environmental, occupational, and lifestyle determinants integrated into individual prevention pathways without centralising personal health data. Intelligence that reaches patients before conditions become irreversible.
Compliant, pre-vetted health intelligence for enterprise workforce management. Absence patterns, occupational risk signals, and preventive programme design built on infrastructure that enterprises can trust with sensitive employee data. Deployed modularly to meet institutional requirements.
Custom infrastructure scoping, model pre-training on partner data, and federated deployment on sovereign cloud. Every health system is different. Our design work bridges the gap between generic capability and institutional reality because in this space, every deployment is an act of co-design.
Loretta co-develops with the regulatory bodies, research institutions, and industry leaders shaping the European health data space. Our infrastructure is designed alongside the policies that govern it, not retrofitted after the fact. We are compliant by design. That means building on top of what exists, because you cannot dismantle a health system and expect progress.
Healthcare is tedious. It requires painstaking attention to detail, incremental progress, and the discipline to resist silver-bullet thinking. We exist to operate as stewards within the system, advancing infrastructure that serves the public good while respecting the complexity of the institutions it must serve.