In this interview, we sit down with Chris Venezia, CEO, and Joseph Kim, Chief Strategy Officer at ProofPilot, to unpack the strategic rationale behind ProofPilot’s acquisition of Lokavant. They explore how the two platforms complement each other, translate the integration into real sponsor value, and address adoption challenges in a data-rich, decision-heavy industry. The discussion highlights market dynamics, change management, and what differentiates a boutique, client-focused approach from larger incumbents.
Moe: After pivots, you frame Lokavant as solving a core problem, not momentum. What concrete fundamentals does the deal address, and how will you measure success in 12–24 months?
Chris Venezia: I’ll frame this around how we apply enduring tech to real-world trial ops. ProofPilot delivers day-to-day trial execution—specific producst that are site- and patient-centric components—while Lokavant adds predictive analytics across IRT/EDC/eCOA and other sponsor data. The acquisition links ground-level activity—document searches, guidance accessed, decisions made—with core study data to yield actionable foresight and risk mitigation tactics. Success milestones will include adoption velocity, time-to-value, reduction of measurable deviations, and signals of improved enrollment or retention, all backed by case studies and ROI that demonstrate how forecasting translates to change management outcomes. We’re not chasing dashboards; we’re pursuing tangible shifts in how trials run and the cause and effect of high performance. Leading indicators will drive interventions, and we’ll track how well those interventions move metrics in real-world deployments.
Joseph Kim: I want to emphasize that success isn’t just about fancy forecasts. It’s about turning those forecasts into concrete actions within sponsor workflows. We’ll measure whether teams adopt new routines, training, and procedures that stem from predictive insights. Our approach combines leading indicators—the behavioral signals we capture on-site and during study interactions—with lagging indicators to provide a trustworthy decision-making framework. Over 12–24 months, we expect to demonstrate faster issue resolution, fewer deviations, and stronger alignment between forecasted risks and on-the-ground actions. This is where the true value is revealed: a repeatable pattern of forecast-informed changes that enhance study performance, rather than a set of isolated numbers.
Moe: There’s hype around predictive tools. What evidence shows forecasts drive real behavioral change, not just dashboards? How does this translate into action?
Chris Venezia: We’ve published white papers linking forecasts to change-management outcomes, but the real signal is our hands-on implementation approach. Most of our team consists of former sponsors who understand day-to-day workflows—they work with customers to embed new processes, train teams, and adjust practices. It’s not enough to provide predictions; we demonstrate how to act on them. We aim to translate insights into concrete steps—training refreshers, procedural clarifications, and workflow tweaks—that sponsors can implement quickly. The integration is designed to push decisions into action, not just into more dashboards. We’re focused on turning predictive signals into practical improvements that teams can own and sustain.
Joseph Kim: In pharma, qualitative narratives and lagging indicators dominate performance characterization; we’re changing that by connecting the leading indicators with outcomes to create a more reliable action framework. The proof lies in repeatable deployments, where teams adopt new practices, monitor results, and adjust in near real-time. Our white papers lay out the theory, but the real impact comes from implementation, where former sponsors and site leaders co-create workflows. The goal is to establish a consistent pattern: identify a predictive signal, implement targeted training or procedural adjustments, and observe a measurable improvement in trial performance across multiple studies.
Moe: How will Lokavant–ProofPilot integration stay complementary, not chaotic, given incumbents’ footprints? What governance or architecture reduces patchwork risk?
Chris Venezia: The core is complementary use cases, not feature overlap. Lokavant excels in reporting, analytics, and prediction; ProofPilot enables day-to-day execution. We aim for a seamless, end-to-end experience with a single dashboard that ties operational actions to predictive insights, anchored in sponsor data. Governance matters as much as technology: our implementation team—comprising former sponsor insiders—ensures the integration aligns with real-world workflows, is reproducible, and minimizes complexity. This isn’t a patchwork; it’s a thoughtfully connected system designed to reduce friction, with clear ownership and guardrails to maintain coherence in data flows and user experiences throughout the trial lifecycle.
Joseph Kim: Architecture must reflect how trials operate in practice. We’re deliberate about data governance, access controls, and change management so the integration supports actual workstreams rather than creating new silos. Our sponsor-grounded approach means we tailor deployments to each study’s realities, reducing the risk of incongruent tools. The result should be a single, trusted view where site staff, CROs, and sponsors see consistent signals and actions, not a jumble of dashboards that require constant translation.
Moe: From top-20 pharma’s lens, how do you counter the startup perception and what differentiates you beyond slogans?
Chris Venezia: Our strength comes from the scale of impact plus a boutique, client-centric approach. Large vendors can deploy at scale but are slow to adapt; we’re nimble, capable of rapid, tailored deployments. We already work with a handful of major pharmaceutical portfolios, which demonstrates that our model scales beyond pilots. The real differentiator is the team—the depth of hands-on, sponsor-informed expertise that translates into practical value, not theoretical promises. We deliver bespoke implementations with measurable outcomes, reinforcing that we’re not just another startup story but a credible, execution-focused partner.
Joseph Kim: The “A-team” advantage matters. In big companies, you hear about A, B, and C teams; with ProofPilot, you only interact with the A-team – that’s all we have. That level of access, combined with practical deployment experience, builds trust and accelerates adoption. If really want to know what people think of the incumbents, try a simple LinkedIn poll about big-vendor sentiment; you’ll see mixed or negative feedback. Our competitive edge is authentic, deep experience, and the ability to move fast without sacrificing quality or customization.
Moe: If adoption stalls, what are real-world fallback scenarios? How do you de-risk adoption and avoid becoming a “nice-to-have”?
Chris Venezia: The risk is real in a crowded market. We counter it with a scalable platform and a robust change-management engine that translates technology into day-to-day value. If adoption slows, our sponsor-focused implementation team reframes workflows and demonstrates measurable improvements, not just theoretical benefits. We emphasize concrete, near-term wins—clear improvements in execution speed, issue resolution, and study clarity—that we can track across studies, portfolios, and sites. By iterating with sponsors, we prove time-to-value and build durable usage.
Joseph Kim: Complexity often drives sites declining to take on a study, according to SCRS; our approach focuses on practical outcomes that simplify complexity. Sites, CROs and Sponsors see immediate value one or two months in. Users quickly realize it’s not just “one more thing” to do, but it’s about optimizing fundamental processes and behaviors in the actual trial workstream. Of course, we’ll monitor usage, gather sponsor feedback, and adjust deployment playbooks to ensure value is realized in real deployments, not just in theory.
Moe: Can you share a real-world use case—beyond dashboards—addressing deviations or training gaps in a live study?
Chris Venezia: Imagine a site deviation where the platform can trace it back to a clear cause: certain content that was not accessed, what guidance was consulted, and how interpretations may have diverged. We’d deploy targeted interventions—such as training refreshers, procedural clarifications, and updated materials—and then monitor for recurrence. This end-to-end visibility enables sponsors to act in near real-time, aligning training and procedures with observed behaviors and data. The system connects day-to-day engagement with outcomes, so corrective actions become iterative and scalable across studies.
Joseph Kim: Real-time traceability means you don’t wait for an audit to know what happened. You can observe how staff and participants interact with content, workflows, resources and procedures, link these interactions to deviations, and intervene promptly. That means fewer repeat mistakes and faster optimization, not only for the current study but across future ones. It’s about turning behavioral signals into proactive prevention, rather than reactive fixes, and demonstrating measurable improvements in study conduct.
Moe: With cloud hype and AI, how do you balance ambition with realism on adoption, and keep the platform from becoming cluttered?
Chris Venezia: We stay grounded in customer needs. It’s not about a flashy AI UI; it’s about a coherent, modular end-to-end platform that staff actually use. The value lies in speed, relevance, and adaptability to sponsor requirements, not novelty. We’re careful to avoid feature bloat by focusing on what moves the needle in day-to-day trial work and how quickly we can deliver it. Our goal is to deliver a streamlined experience that scales with trials while remaining navigable for both site teams and sponsors.
Joseph Kim: Real-world deployment matters. Our implementation team brings sponsor-grade perspectives, ensuring that integration becomes seamless and cohesive. We prioritize usable workflows and repeatable deployments, ensuring the ecosystem remains coherent rather than sprawling. The goal is to create a trusted, integrated environment where decisions are informed by both data and behavior, without overwhelming users with unnecessary modules or features.
Moe: What are the hard risks if adoption doesn’t materialize, and how do you mitigate them?
Chris Venezia: As you said, the risk is becoming a “nice-to-have” in a crowded market. We mitigate this with a robust, end-to-end platform and a strong change-management engine that translates tech into observable value. The market is consolidating around sites, CROs, and sponsor networks, seeking actionable insights, so sponsors who don’t adapt risk losing ground. Our boutique approach, speed, and tailored support aim to deliver measurable outcomes faster than many incumbents.
Joseph Kim: If value isn’t demonstrated in real deployments, stories stay theoretical. We emphasize measurable outcomes, repeatable deployments, and rapid iterations to prove impact. The platform’s success hinges on adoption—so we continuously collect feedback, refine workflows, and show concrete improvements in study execution, decision speed, and risk mitigation. That practical track record is what keeps sponsors engaged and committed. We have amazing Customer Satisfaction scores, testimonials, and quantitative value adds – for example, study managers are reporting getting a half day a week back in free time.
Moe: Closing reflection—what makes this ProofPilot–Lokavant combo uniquely positioned to influence the trials landscape, and what should readers take away?
Chris Venezia: The magic is connecting operational execution with data science at scale, without forcing a one-size-fits-all approach. We adapt quickly, scale with trials, and earn trust with depth, speed, and a capable team backing change. The differentiator is moving from lagging indicators to a blended set that includes leading indicators, enabling real-time decisions across sites and data ecosystems, not just forecasts.
Joseph Kim: This is about turning insight into action across the entire trial continuum. We’re not just forecasting; we’re shaping behavior and decisions in real time, with end-to-end visibility that spans site activity and data systems. The result is a practical framework for more reliable trials, faster decisions, and measurable outcomes that matter to sponsors, sites, and patients alike.
Moe Alsumidaie, MBA, MSF, is founder and Chief Editor of Vanguard Publications, which publishes Clinical Trial Vanguard, Pharma Vanguard and BullScope, and Head of Research at CliniBiz. He has two decades in clinical trial operations and data science, with earlier roles at Genentech, Abbott Vascular and Stanford University Medical Center, and is a guest lecturer in clinical trial sciences at Rutgers University.
