The Zurich-based startup, which raised €3 million to unify previously fragmented trial data, has secured a larger round to build artificial intelligence agents that actively manage the complex operational layer of running clinical trials.
Clinical trials, by almost any measure, are one of the most data-intensive and least efficiently managed processes in modern medicine. A single Phase III trial can simultaneously generate data across dozens of sites, hundreds of variables, and multiple regulatory frameworks.
The software tools that research groups use to manage this complexity were developed in an earlier era, when the primary goal was memory rather than intelligence.
RiviaA Zurich-based company that has been building a unified data platform for biotech clinical trials since 2022 announced today that it has raised €13 million in a new funding round to expand what it describes as an agent data engine, in which artificial intelligence agents proactively uncover insights, flag anomalies and help coordinate the operational level of simple data storage and visualization.
The company raised €3 million in funding led by Speedinvest in June 2024 to build its core data infrastructure. The new round is a significant step in ambition and capital, reflecting both Rivia’s progress with early biotech customers and a broader shift in enterprise AI to systems that operate, not just analysis.
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Problem with test data
The main problem that Rivia solves is fragmentation. Clinical trial data flow from electronic data collection systems, patient wearables, laboratory instruments, site management software, and regulatory documents, often through different vendors, in different formats, with different update cycles.
Research teams spend a significant portion of their time simply reconciling these data sources rather than extracting insights from them.
The Rivia platform integrates these data streams into a single environment, as described in their previous materials. The AI layer, which the company now calls an agent data engine, goes a step further by using agents based on a large language model to proactively answer questions about trial status, identify enrollment risks before they turn into delays, and flag data quality issues.
The company said it has deployed the platform with biotech customers with active trials, though it did not disclose the number of customers or the scale of trials covered.
Agent AI responds to regulated environments
Clinical trials are conducted in one of the most highly regulated environments in any industry. The US Food and Drug Administration and the European Medicines Agency impose strict requirements on data integrity, audit trails and approval documentation.
Any AI system operating in this environment must not only perform well, but also be explainable and testable in a way that general-purpose AI tools are not.
This regulatory complexity is both Rivia’s problem and its moat. A company that can build AI agents that operate within FDA and EMA compliance frameworks, not around them, has a defensible position that a general-purpose AI tool cannot easily replicate.
It was not clear from public communications whether Rivia has already achieved this level of regulatory readiness or is progressing towards it.
The broader market for AI in clinical trials attracted significant capital in 2025 and 2026, with competitors including Medable, Veeva, and a cohort of smaller startups competing in different parts of the trial stack.
Rivia’s contention is that the data layer, the substrate that connects everything, is the highest value position in architecture. With €13m currently in the bank, it has the runway to test this thesis.
