As pressure mounts on the pharmaceutical industry to deliver faster and more cost effective drug development, a new generation of infrastructure startups is emerging to modernise one of healthcare’s most complex systems. Zurich based Rivia is among them, and it has now raised $15 million in Series A funding to expand its clinical trial intelligence platform and bring AI deeper into trial operations.
The round was led by Earlybird, with participation from Defiant and existing investors including Speedinvest, Amino Collective, and Nina Capital. The investment comes at a time when the clinical trial ecosystem is facing both increasing regulatory demands and declining economic returns, creating urgency for more efficient and scalable solutions.
Building a reusable intelligence layer
Over the past three years, Rivia has focused on developing what it describes as a reusable intelligence layer for clinical trials. At its core is a data engine designed to integrate and harmonise large volumes of fragmented clinical data in real time.
Clinical trials generate data from a wide range of sources including laboratories, imaging systems, wearable devices, and patient reported outcomes. However, these datasets are typically stored across multiple vendors and systems that lack interoperability. As a result, teams often rely on manual processes or custom pipelines to combine and analyse information.
Rivia’s platform addresses this by connecting to disparate data sources and applying trial specific scientific logic through a library of reusable configurations. This enables consistent interpretation of data across studies and feeds structured insights directly into operational workflows, allowing teams to make more proactive decisions.
Introducing AI agents for clinical workflows
Building on this foundation, the company is now launching a suite of embedded AI agents designed to automate key aspects of clinical trial operations. The first of these agents, called Spark, converts natural language inputs into publication grade clinical visualisations, enabling faster and more accessible data analysis.
Future agents are being developed to support data quality monitoring and oversight. These systems are designed to detect deviations earlier, prioritise issues intelligently, and provide structured and auditable actions. By embedding AI directly into workflows, Rivia aims to reduce manual effort and improve the reliability of trial execution.
The company’s approach reflects a broader shift toward agentic systems that not only analyse data but also support decision making and operational processes in real time.
Addressing a fragmented infrastructure
Despite advances in biotechnology and drug discovery, the infrastructure supporting clinical trials has remained largely unchanged. Many organisations still depend on spreadsheets and disconnected systems to manage data, creating inefficiencies and delays.
The problem is compounded by the growing scale and complexity of trials. Data volumes have increased significantly over the past decade, while modern studies incorporate diverse data types from multiple specialised sources. Traditional systems, which were originally designed for data capture and compliance, are not well suited to this environment.
Rivia’s strategy is to rebuild the underlying data layer, enabling integration and analysis across multiple systems without relying on manual workarounds. By structuring data according to the logic of each trial, the platform provides a consistent foundation for both analytics and AI driven applications.
Economic and regulatory pressures
The timing of Rivia’s funding reflects broader changes in the pharmaceutical industry. Regulatory frameworks are evolving to require more proactive risk management and compliance, while the financial returns from drug development have declined over the past decade.
At the same time, the number of therapies that successfully reach the market remains limited, highlighting inefficiencies in the development process. Clinical trials represent one of the largest cost centres for pharmaceutical companies, making them a key target for innovation.
By improving data infrastructure and automating workflows, Rivia aims to reduce trial costs and accelerate timelines. The company believes that better data handling and earlier insights can lead to more efficient studies and improved outcomes for patients.
Scaling toward a new trial paradigm
Looking ahead, Rivia sees its platform as a foundation for more advanced clinical trial models, including adaptive and decentralised designs. As data becomes more accessible and actionable, AI systems could enable trials to adjust dynamically based on emerging evidence.
While adoption will require time and regulatory validation, the potential impact is significant. More efficient trials could reduce costs, increase the number of viable research programmes, and bring new therapies to market faster.
With fresh funding and growing demand, Rivia plans to expand its team across Zurich and Boston while continuing to develop its platform. By combining a unified data engine with embedded AI agents, the company is working to redefine how clinical trials are conducted, moving the industry toward a more integrated, scalable, and data driven future.