In this video interview, Raj Indupuri, CEO and co-founder of eClinical Solutions, describes how giving sponsors the ability to inject their own SOPs, protocols, and governance frameworks into AI systems is the key to building the trust needed for real-world adoption.
In a recent video interview with Applied Clinical Trials, Raj Indupuri, CEO and co-founder of eClinical Solutions, discussed what the FDA's push toward real-time clinical trial data
Indupuri described the shift as requiring not just a technology upgrade but a fundamental change in how data quality and governance are practiced. In the current model, quality is enforced at milestones—database lock, interim analysis, submission preparation. In a continuous review environment, governance must be built into every step of the trial life cycle, with traceability and alignment across clinical operations, data management, safety, and statistics teams that today still largely operate in silos.
On RBQM, he argued that the industry's progress has been real but remains primarily study-level and fragmented, and that the next evolution is enterprise-wide risk and quality management where all teams connect to a single infrastructure and monitor continuously. He pointed to AI agents as a key accelerant—eClinical is actively investing in agentic AI across the clinical data life cycle—but was equally focused on the governance challenge AI introduces. His answer to the black-box problem is what he calls a glass-box approach: giving sponsors the ability to inject their own SOPs, protocol context, and governance frameworks into AI systems, and providing full traceability of outputs so users can validate and trust what the systems produce.
Indupuri closed by addressing the blurring boundary between clinical trial data and real-world evidence, arguing that organizations that build infrastructure capable of harmonizing both in real time will be significantly better positioned for the continuous, data-driven regulatory environment now taking shape.
