Why I Built AI Teammates for Clinical Research and What It Means for Sponsors

If you're working in clinical development today, you already know the hard truth: clinical trials are structurally inefficient. They’re slow, expensive, and fragmented. And at the center of it all is a human bottleneck we don’t talk about enough: the study coordinator.

Before I built Tilda, I spent four years on the sponsor side, running global studies across more than 600 patients. I sat through site selection meetings, CRO reviews, protocol revisions, data reviews, you name it. But something didn’t sit right. I knew what the CROs and vendors were telling me. I knew what the dashboards said. But I didn’t know what it felt like to run a trial from the ground.

So I did something unusual.

After exiting my last biotech company, I acquired a network of clinical research sites. I became a coordinator. Not in theory, not on paper, but in everyday real life. I showed up every day for two years. I did the regulatory packets. I managed the startup delays. I navigated the protocol deviations. And I saw firsthand just how wide the gap is between sponsors’ expectations and site realities.

That experience changed everything for me. It made one thing unshakably clear: if we want to modernize trials, we need to start by making the coordinator 10x more effective. Not just faster, but supported, focused, and unburdened.

The Coordinator is Both the Bottleneck And the Key

In any clinical trial, the coordinator is Atlas. They hold up the entire trial infrastructure (regulatory, data entry, scheduling, PI communication, patient logistics) while being the lowest paid and most overburdened member of the team. When things fall through the cracks, it’s rarely about motivation. It’s about capacity.

And when you look at the tools we’ve given them? You’ll realize we’re still expecting 21st-century performance from 1990s systems.

One simple example: document management. When a new ICF gets approved by the IRB, what does the sponsor imagine? That the site is on it in two minutes. What really happens? The email might go unnoticed for days. Nobody re-signs the form. It turns into a deviation. Now you need a re-consent. The whole thing spirals.

It’s not because the coordinator is negligent. It’s because they’re juggling too much. They're asked to be regulatory experts, data managers, patient recruiters, and problem-solvers all at once with little help and less automation.

That’s where I believe AI comes in.

AI Teammates, Not Just Tools

I didn’t start Tilda Research to sell software. I started it to modernize clinical research to enable it to bring more cures to patients, and to do it faster and at a scale far greater than we could previously have imagined. I believe we can accomplish this with AI teammates, tools that do actual trial work alongside human staff. Think of it this way: every coordinator has a stack of tasks that drain time and mental energy but don’t require judgment or relationship-building. That’s the zone where AI thrives.

Take study startup. Instead of having a human parse the startup packet, fill out forms, route them for signatures, and upload to the TMF, we built an AI teammate that can do all of that. It reads the email, extracts the documents, pre-fills forms based on site info, requests e-signatures, and files everything.

Or consider data entry. Every site today enters the same data twice: once into source and again into the EDC. Our AI teammate handles that second step. It pushes source data into any major EDC platform, eliminating hours of manual transcription and reducing error rates.

And then there's regulatory maintenance such as ICF version control, continuing reviews, IRB updates, etc. The AI doesn’t just store documents; it monitors them. It checks portals. It flags what’s missing. It a vigilant QA assistant that never sleeps.

From Insight to Scale

We didn’t build this in a vacuum. We learned it by living it.

Because we operated our own sites, we weren’t guessing what coordinators need. We knew from direct experience. We sat in the chaos of overlapping studies, understaffed teams, and startup delays. We didn’t ask “How can we get coordinators to do more?” We asked, “How can we take things off their plate, without compromising quality?”

Now, across our current sites and sponsor partners, we’re seeing the impact.

With AI teammates, sponsors can launch more studies without hiring more people. Sites are scaling from two locations to ten. Biotechs are telling investors, “We can do more with less, and we can prove it.” In fact, one of our sponsor partners recently raised a second round of capital precisely because they became demonstrably more efficient.

That’s the shift. AI isn’t just helping people do their jobs. It’s enabling entirely new strategies.

Why Sponsors Should Care

If you're in pharma R&D, here's why this matters to you: your timelines, your data quality, and your enrollment rates are all downstream of how effective the coordinator is.

Yet sponsors rarely engage with the work of the coordinator directly. It’s abstracted through CROs or CTMs. But when you actually visit sites as I did, you realize how brittle the system is. It’s built on people who are overwhelmed and under-supported.

This creates risk: delays, deviations, dropouts, and friction.

But it also creates opportunity.

Because if you can make the coordinator more effective, if you give them a digital teammate that handles regulatory, data, finance, you reduce site burden, increase throughput, and unlock scale. And that changes the economics of R&D.

What Comes Next

We’re still early. But the signals are clear. The future of clinical trials won’t be built on static software licenses. It will be built on AI teammates that work alongside your team to get things done. Not dashboards. Not reports. Real work.

And I believe this future won’t just reduce cost. It’ll expand capacity. More trials. More sites. More patients enrolled. Faster answers.

The productivity unlock we’re seeing isn’t marginal—it’s exponential. And as the industry shifts from fearing AI to using it, the gap will widen between those who embrace it and those who wait.

I didn’t come into clinical research to patch holes. I came here to reimagine how we run trial based on what I saw, firsthand, from both sides of the table. From biotech executive to coordinator.

We don’t need another vendor. We need new teammates. That’s what we’re building. 

Want to see AI teammates in action? Book a time to see for yourself.