Rivia launches Data Findings, secures $15 million Series A

By Grace Kim

Rivia said it secured an oversubscribed $15 million Series A in March 2026, led by Earlybird Venture Capital, as it launched Data Findings, an AI module designed to automate clinical data review and shorten delays between trial completion and data analysis.

Rivia said the round included Defiant and existing investors Speedinvest, Amino Collective and Nina Capital. The financing will support expansion of the company’s clinical trial intelligence platform and embedded AI capabilities.

What does Data Findings automate?

Rivia, a Zurich-based clinical trial intelligence technology company, says Data Findings operates as an add-on to Rivia Core. Rivia Core combines information from different clinical trial systems and vendors in a unified data environment.

Rivia says the module automates checks across multiple sources, allowing research teams to identify inconsistencies and other potential problems while a study remains underway.

Rivia says clinical trials collect large volumes of patient information across numerous forms, systems and research sites. Traditional electronic data capture platforms can identify straightforward entry errors, while complex checks across multiple forms and data types often require specialized programming or manual review.

Rivia built Data Findings for those multi-source checks. Its agentic AI rule builder lets teams describe validation requirements with natural-language instructions instead of writing custom software.

The platform converts those instructions into validation checks that run continuously as clinical trial information changes. In Rivia’s system, AI agents identify, group and prioritize findings for clinical teams to review.

Rivia says the module also maintains version controls, approvals and audit records designed to support data integrity and regulatory oversight. Rivia believes the approach can reduce cleanup after the last patient completes a study.

Rivia CEO and co-founder Erik Scalfaro said:

“The increasing complexity of drug development requires a fundamentally different approach to clinical data infrastructure. Unresolved data issues regularly delay the transition from clinical trial completion to data analysis and regulatory submission up to several months.”

“Through real-time, automated review of clinical trial data, Data Findings helps clinical teams flag issues during the study, rather than leave them for the final clean-up.”

Rivia’s design shifts intensive validation work from the end of a trial toward continuous checks while the study is running.

How much time could Scancell save?

Rivia identifies Scancell, a UK-based clinical-stage biotechnology company developing cancer immunotherapies, as Data Findings’ design partner and first adopter.

Scancell expects to reduce its end-of-study data-cleaning period from eight weeks to approximately two weeks. Under Scancell’s estimate, that six-week improvement could allow the company to complete database-lock procedures earlier, provided the expected operational improvements materialize.

Joe Thornton, vice president of clinical operations at Scancell, described the Phase 2 deployment:

“Adopting Rivia’s AI-driven clinical data analysis tool Data Findings has enabled Scancell to translate its Phase 2 clinical trial data with greater granularity, confidence and efficiency.”

Thornton said the tool also facilitated in-study data cleaning and quality checks. He said Scancell expects to reach Last Patient Last Visit (LPLV) with substantially improved data quality and the potential to bring forward end-of-study database lock by six weeks.

Scancell’s test is concrete: reach LPLV with better data and bring database lock forward by six weeks.

How fast are clinical trial data volumes growing?

Research from the Tufts Center for the Study of Drug Development, cited by Rivia, says Phase 3 studies collect an average of 5.9 million data points per protocol. The same research says data volumes have increased approximately 11% annually since 2020.

Rivia says delays can create significant financial consequences for pharmaceutical developers, particularly when they affect the timing of regulatory submissions or potential product commercialization.

Rivia says Data Findings applies AI throughout a study to find issues rather than concentrating substantial review work at the end. The numerical pressure is clear: Phase 3 teams manage more data, and Rivia says unresolved issues can add months before regulatory submission.

What does the $15 million round support?

Rivia says the broader program behind Data Findings seeks to improve clinical data quality, reduce repetitive work and accelerate the development of new medicines. The product extends the company’s existing data management platform with embedded AI capabilities.

Scancell’s projected six-week reduction remains an expectation, not a reported outcome. Rivia’s $15 million financing will fund the platform’s expansion, while Scancell provides the first operating test of its central efficiency claim.

If Scancell delivers the projected six-week reduction, Data Findings will have moved from a projection to a measured operating result. Broader adoption will depend on whether other trial teams reproduce that gain.