Aridhia provides a cloud‑based Trusted Research Environment (DRE) that lets healthcare and life‑science organisations securely share health data and run AI‑driven research while maintaining governance and compliance. The platform supports FAIR data discovery, governed access workflows, and federated analytics, and is already used by research hospitals and consortia in over 80 countries, including FDA‑qualified kidney safety biomarker studies.
Funding
$783.4K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Researchers and healthcare organisations need to analyze large volumes of sensitive health data, but strict privacy regulations, fragmented data sources, and the need for secure governance make collaborative AI-driven research difficult.
Solution
Aridhia offers a fully certified, cloud‑based Trusted Research Environment (DRE) that enables secure sharing, discovery, and analysis of health data at any scale. The platform provides FAIR‑compliant metadata catalogs and governed access workflows to ensure data provenance and regulatory compliance, including alignment with the European Health Data Space. Integrated secure AI tools, such as the offline AIRA framework and AI sandbox, allow researchers to develop and run machine‑learning models without exposing data. Federated analytics let multiple sites collaborate while keeping data locally controlled, and built‑in workspaces deliver on‑demand analytical environments with pre‑installed statistical and data‑science tools.
Target Audience
Primary users are research hospitals, global biomedical consortia, and life‑science organizations that need to conduct collaborative, AI‑enabled health data research while meeting strict governance and privacy requirements.
Features
- Cloud‑native TRE with ISO 27001 and HITRUST certification for stringent security and compliance
- FAIR metadata catalog and searchable data discovery across global consortia
- Governed access controls, audit trails, and airlock mechanisms for secure data ingress/egress
- Secure offline AI framework (AIRA) and sandbox for evaluating LLMs and machine‑learning models within the TRE
- Federated analytics infrastructure that enables cross‑institutional studies without moving raw data
- On‑demand workspaces with native R, Jupyter Lab, statistical modules, and Collabora Office for collaborative analysis
- Scalable licensing model supporting individual researchers to enterprise‑wide deployments