Condor

Our best product roadmap decisions start as a frustrated comment on a customer call. For example, a clinical finance leader told me she had no way to check a payments vendors monthly total against what her sites actually did. Investigator grants represent up to half of per-trial cost, and too many teams were stuck choosing between taking the vendors word for it, or reconciling thousands of lines by hand in spreadsheets. That's why Im excited to introduce AI-powered investigator grants intelligence.Customers can upload vendor payment data, and Condor reconciles it against the contracts and trial data already in the platform. Condor automatically surfaces overbilling and pricing discrepancies, missing and unmatched payments, and site and vendor billing patterns. Because payment history is now connected to trial performance, those patterns also feed Condors forecasting, giving finance and clinical operations teams projected spend grounded in how vendors actually bill rather than in invoices alone. That's how we operate at Condor: listen closely, build what removes the most manual work, and ship it fast. September's Clinical Finance AI Agent. Now this. Thank you to every customer who pushed us here! See the link to the blog post in the comments.

“Tons of data and absolutely no information.” That's how an executive overseeing more than $1 billion in annual clinical trial spend described his financial systems to me last week. His forecasts may be built to the penny, but they're still wrong by millions. One study slipped nearly 6 months. The spend forecast barely moved. Another returned $15 million in unused budget. Meanwhile, a different program had been sitting on hold since September waiting for funding. The money existed. The visibility didn't. And this isn't an isolated story. I'm hearing versions of it in nearly every room I'm in. We've built an industry that can track clinical trial spending down to the decimal, but can't reliably tell us what a trial will actually cost, when the money will be spent, or where capital could be redeployed. The consequences go far beyond financial inefficiency. A promising therapy doesn't have to fail scientifically to never reach a patient. It can stall because finance and clinical operations are working from two different realities. It can lose funding because a forecast that hasn't kept pace says the budget isn't there. AI is accelerating drug discovery. But discovering more therapies doesn't mean more therapies reach patients. The next bottleneck isn't scientific discovery alone. It's the ability to turn promising science into funded, executable clinical programs. That requires clinical and financial operations working from the same reality. That's what we're building at Condor.

Getting the "why" behind the numbers in seconds rather than hours is the single most requested capability I've heard from our customers over the past year. That’s why we just unveiled our new clinical finance AI agent. It reasons across an organization’s full budget and forecast history in Condor to explain the “why” behind the numbers, identifying what’s driving a variance between actuals and forecast or the cost to complete a given trial, in seconds rather than hours. It also runs full what-if scenario planning, modeling changes to site mix, enrollment timing, or financial investment, and then builds the resulting model directly in the platform. It’s faster and more accurate and cost effective than manual analysis, and empowers R&D teams to make decisions faster and more confidently. What I love about this launch is what it says about how we operate at Condor: customers told us what they needed, and we built it. Check it out today! See the link to the blog post in the comments.

The number you sign with your CRO is rarely the number you pay. I spent time on the CRO side building change order budgets. Here's what I learned: a sponsor asks what it would cost to add 15 patients, and a few weeks later gets one ballpark number with a 20% buffer on top. It's the CRO's answer, on the CRO's timeline. Meanwhile: • Protocol amendments, the primary driver of CO's, now hit 76% of trials (82% in Phase 3) • The average Phase 3 change order runs about $535K • Change order volume has doubled while finance headcount hasn't In a recent demo, we ran a $2.25M Phase 3 change order through Talon, our internal AI analysis platform. About 94% of the dollar delta was unjustified, unsupported, or needed clarification. Then we modeled the same scope change ourselves in Condor, before the CRO's number arrived: $629K vs. the $2M+ they proposed. That gap is the difference between reacting and negotiating. I wrote up how sponsors can review change orders faster, catch retroactive billing, and walk into the conversation with their own number: https://lnkd.in/efMHcSMk

For decades, science was the bottleneck in drug development. That’s changing fast. AI is accelerating discovery. The patent cliff is forcing pipelines to expand. Biopharma is about to have more candidates to fund than ever before. The bottleneck didn’t disappear. It moved from the lab to the ledger. Every one of those candidates still has to be budgeted, forecasted, funded, and managed. And the financial infrastructure responsible for doing that still largely runs on spreadsheets. Today, when a clinical forecast and actuals diverge, R&D Finance and Clinical Operations teams can spend days or weeks pulling apart data from ERPs, CTMS, EDC systems, CROs, and spreadsheets just to answer basic questions: Why did the number change? Then comes the harder question: What should we do about it? Change the site strategy? Model a different enrollment curve? Shift investment between programs? Each decision creates another model to build and another scenario to reconcile. Today, we’re changing that by introducing the world's first Clinical Finance Agent purpose-built for biopharma R&D. Built on the clinical and financial knowledge graph we’ve spent years developing alongside Big 4 accounting firms, the agent can investigate a variance, uncover what drove it, and build the resulting scenario directly in Condor. What used to take days can happen in seconds. - Ask why enrollment spend is running above forecast. - Ask what changed. - Ask what happens if enrollment moves three months. - Ask it to model the impact. The answer isn’t another spreadsheet. It’s the model. This is one of several agents in Condor AI Workflows — a team of AI agents that automate workflows, like forecasts, closes, accruals, and change order management. And it's the realization of the vision I had when I founded Condor: The numbers built by our engine. The workflows run by AI. The why behind the numbers uncovered in seconds, not weeks. No more manually managing the finances behind drug development. If you work in Clinical Operations or R&D Finance and are still doing this by hand, I’d love to show you what we've built. And if you want to help us build the next agent, we’re hiring. See the link to the blog post in the comments.

Our VP of Engineering Jay Tailor sat down with CTO Studio and he said something I want more people to hear. AI doesn't remove the need for real engineering. It raises the bar. Anyone can pull together a few data sets, throw them in the cloud, and demo something impressive today. That's not the hard part anymore. That’s called vibe coding. The hard part is whether it still works in four years. Whether the context holds. Whether the caching layer, the audit logs, the feedback loop are actually built, not assumed. Jay put it well: the basics of engineering that existed 50 years ago don't disappear because you added a model on top. If anything, skipping them just gets more expensive, faster. That's the discipline we built Condor around. A deterministic core for the math that can't be wrong — budgets, patient enrollment curves, site performance. AI layered on top for the analysis that used to take teams days and now takes minutes. I didn't hire Jay to move fast. I hired him to build the financial intelligence layer for pharmaceutical R&D, one that's still standing stronger than ever years from now. Our engineering team is growing. If you want to build something that lasts, actually matters, and fundamentally shapes how the pharma industry innovates, let's talk. Link to the interview in the comments.