Rivia raises €13 million for agentic AI to manage clinical trials

Rivia, a Swiss startup, raised approximately €13 million in a new funding round to develop agentic AI for clinical trials. The Zurich-based company wants to automate not the science itself, but the most painful operational layer in drug development: the collection, reconciliation, verification, and interpretation of data that today remain scattered across dozens of systems.

Why the Market Is Broken

Clinical trials are among the most data-saturated processes in medicine. Even a single large Phase III study can simultaneously pull information from research centers, laboratories, patient diaries, wearable devices, imaging, genomics, and regulatory documents. The problem is that these streams typically live with different vendors, arrive in different formats, and update at different speeds. As a result, teams spend time not on analysis but on endless manual assembly of a unified picture.

According to Rivia, over the last decade the volume of data in clinical trials has grown by more than 400%, but the underlying infrastructure has barely changed. Large systems like Veeva and Medidata historically solved the problem of storage and regulatory compliance, not real-time integration of heterogeneous sources. In practice, companies often still export files from multiple systems, consolidate them in Excel, or build separate pipelines for each new protocol.

What Rivia Is Building

Rivia has been operating since 2022 and initially built a unified data layer for biotech teams, then began adding AI agents on top of that foundation. In June 2024, the startup raised €3 million in seed funding, and now has closed a larger Series A round, which according to various publications amounted to $15 million, or approximately €13 million. The round was led by Earlybird; other participants included Defiant, Speedinvest, Amino Collective, and Nina Capital. The funds will go toward growing teams in Zurich and Boston and launching new embedded agents.

"If you don't first establish a proper data structure, AI will work on poorly organized information and deliver unreliable results," explains

Rivia CEO Eric Scalfaro.

Currently, the Rivia platform includes several key layers:

• a unified layer of normalized data for disparate sources
• a library of reusable configurations for specific study logic
• the Spark agent, which converts natural language queries into clinical charts and summaries
• agents for proactive monitoring of data quality and anomalies
• processes with transparent audit trails of actions that can be verified in the regulatory framework

Separately, Rivia references results from a Phase 2 study, where Spark helped the sponsor team with typical clinical review tasks—from analyzing adverse events to building cohorts and preparing descriptive summaries. According to the company, the average time for a manual task was 47 minutes, while Spark's response took around 2 minutes. In total, this yielded 91% time savings and freed up approximately 20 hours, which the team could spend not on data mechanics but on clinical decisions.

The company has not yet disclosed its customer count, but asserts that the platform is already being used in active trials.

Where Agents Will Be Useful

The most interesting aspect of Rivia's story is not just another AI assistant that delivers a nice answer, but an attempt to embed agents in a heavily regulated environment. In clinical trials, a "smart chat" that provides a polished response is not enough. What matters here is explainability, audit trail, validation, version control, and compliance with FDA and EMA requirements.

Against this backdrop, regulatory pressure is also growing: the industry increasingly expects tools that help manage risks and compliance proactively, rather than after the fact. In practice, such agents can address several expensive and slow tasks simultaneously: flag data quality problems in advance, identify patient recruitment risks before deadlines slip, help teams build visualizations and summaries faster as the study progresses, and structure actions so they can be audited later.

If this approach scales, the wins will benefit not only CROs and biotechs, but also drug development programs themselves, where delays quickly translate into millions of dollars in losses.

What This Means

Rivia is betting on a clear thesis: the next stage of AI in healthtech is not text generation on top of chaotic files, but agentic systems that understand clinical trial logic and operate within regulatory frameworks. The company sets an ambitious goal to reduce trial costs by 50%; if it can prove the reliability of this approach in real programs, the market will gain not another dashboard, but a new operational layer for drug development.