We didn't set out to build a company. We set out to survive—and then thrive—in medical sales. What started as a side project to make our own lives easier turned into RepPrep.ai. This is our story: the grind, the breakthrough, and why we believe every rep deserves the same advantage we had.
The Daily Grind: Medical Sales Without Data
Both of us have spent over a decade in medical sales. We've carried bags, logged miles, and learned the hard way that success in this industry isn't just about relationships—it's about preparation. But for years, preparation meant the same exhausting routine.
We'd wake up early, drive to the first account, and hope we'd remembered enough from the last visit. We'd dig through CRM notes in the parking lot. We'd try to recall which physician cared about which study, who had raised which objection, and which accounts were worth the extra effort. Some days we'd walk in prepared; many days we'd wing it. The difference showed. When we were prepared, calls went somewhere. When we weren't, we got polite nods and quick exits.
The real frustration wasn't the occasional bad call—it was the ceiling. We knew there was more opportunity in our territories than we were capturing. We knew some physicians were high-volume performers we'd never properly prioritized. We knew we had the clinical knowledge and the work ethic. What we didn't have was a way to turn the scattered data—claims, publications, payments, CRM—into a clear plan before every single call.
The "Aha" Moment: Data Could Change Everything
The turning point came when we started pulling together data ourselves. Not in a formal way at first—just experiments. What if we could see procedure volumes before we walked in? What if we knew a physician's research interests from their PubMed history? What if we could see Sunshine Act payments and understand their relationships with other companies?
We'd spend evenings and weekends building spreadsheets, pulling from public sources, and stitching together whatever we could find. The first time we walked into a call with that kind of intelligence—knowing the physician's volume, their recent publications, and their payment history—the conversation was different. We weren't guessing. We were prepared. And the physician noticed.
That was the "aha" moment. Data wasn't a nice-to-have. It was the difference between a generic pitch and a tailored conversation. It was the difference between "I'll think about it" and "Let's do it." We started using our makeshift system for every call. Our close rates improved. Our confidence improved. And eventually, we both made President's Club.
Building a Side Project That Became RepPrep.ai
We didn't stop there. We kept refining the approach. We added more data sources—Medicare and commercial claims, territory mapping, CPT code filtering. We built workflows that could generate a pre-call brief in minutes instead of hours. What had taken us an evening to compile for one physician could now be done in seconds for dozens.
Friends and colleagues started asking how we were doing it. "What are you using?" "Can you show me?" We'd share our spreadsheets and our process, but it was clunky. Not everyone had the time or the technical chops to build what we'd built. That's when we realized: if this worked for us, it could work for others. The side project needed to become a product.
RepPrep.ai was born from that realization. We took everything we'd learned—the data sources that mattered, the workflows that saved time, the insights that closed deals—and built a platform that any rep could use. No more manual aggregation. No more parking-lot prep. Just a clear, data-backed plan before every call.
How Data-Driven Planning Helped Us Hit Quota and Make President's Club
The impact wasn't theoretical. It showed up in specific ways.
Prioritization. We stopped wasting time on low-volume physicians who "felt" important. Claims data showed us who actually performed the procedures that mattered. We reallocated our visits accordingly. More face time with high-value targets. Fewer wasted drives.
Tailored conversations. When we knew a physician had published on a specific topic, we could reference it. When we knew their volume was growing, we could speak to scaling their practice. When we knew they'd received payments from a competitor, we could anticipate their familiarity with the space. Generic pitches became rare. Tailored conversations became the norm.
Objection handling. Pre-call planning surfaced common objections before we walked in. We'd prepare evidence, studies, and talk tracks. When the objection came—and it always did—we were ready. That readiness built credibility. Physicians started seeing us as partners, not pushers.
Consistency. The biggest shift was doing it every time. Not just for the "important" calls. For every call. That consistency compounded. We built reputations as the reps who showed up prepared. That reputation opened doors and kept them open.
Territory optimization. Claims data revealed physicians we'd been under-calling. Some were in "inconvenient" locations we'd deprioritized. Others were in specialties we'd assumed were saturated. When we mapped procedure volume against our call frequency, the gaps were obvious. We redistributed our time. The result wasn't just more calls—it was more calls with the right people.
President's Club wasn't luck. It was the result of hundreds of better-prepared calls. Data gave us the edge. We want every rep to have that same edge.
The Decision to Commercialize RepPrep.ai
Taking RepPrep.ai from a personal tool to a commercial product wasn't an obvious choice. We had careers. We had stability. Building a company meant risk. But we kept coming back to the same question: why should preparation be a luxury? Why should only reps with the time and technical skill to build their own systems have access to this kind of intelligence?
The medical sales industry is full of talented people working harder than they need to. They're digging through CRM, Googling physicians, and hoping they've got the right message. We'd been there. We knew there was a better way. So we decided to build it—not just for ourselves, but for everyone in the field. We brought in the data sources that had made the difference for us: Medicare and commercial claims, PubMed, Sunshine Act, territory mapping. We designed it for the rep who's in the car between calls, who has five minutes before the next office, who needs the answer now—not in an hour.
Our Vision for the Future of Medical Sales
We believe the future of medical sales is data-informed and relationship-driven. AI and analytics will handle the heavy lifting—aggregating data, surfacing insights, suggesting talk tracks. Reps will focus on what they do best: building trust, understanding needs, and closing deals. The best reps will always be the ones who connect with people. But the best-prepared reps will be the ones who show up with the right data at the right time.
RepPrep.ai is our contribution to that future. We're not trying to replace the rep. We're trying to make every rep more effective. If our story resonates—if you've felt the frustration of under-preparing or the ceiling of not having the right data—we built this for you. Here's to more prepared calls, better conversations, and a path to President's Club that doesn't require burning the midnight oil to get there.
Frequently Asked Questions
Who founded RepPrep.ai?
RepPrep.ai was founded by Eddie Dix and Brandon Hoover, both with over a decade of experience in medical sales, after building their own data-driven pre-call planning process that helped them each reach President's Club.
What made the founders start using data before it was a product?
They started experimenting with pulling together procedure volume, PubMed publication history, and Sunshine Act payment data manually — building spreadsheets on evenings and weekends — because existing tools didn't connect those data sources into one usable view before a call.
How is RepPrep.ai different from tools built by non-sales-background teams?
RepPrep.ai was designed by reps who lived the manual research grind themselves, which shapes product decisions around what actually saves time in the field rather than what looks useful from an analyst's desk.