Nim Fox

The best product decisions we make don’t start in a roadmap meeting. They start on customer calls, when a clinical operations or FP&A leader tells us what’s still eating their week.

Over the past year, one topic kept coming up in almost every conversation: investigator grants intelligence.

The problem you described

Investigator grants account for up to 50% percent of total per-trial cost. A single mid-sized trial can generate tens of thousands of payment lines. Teams had two options, and neither worked:

• Take the vendor’s word for it. Most teams outsource grant payments to CROs or payments vendors who typically share summary reports — sometimes only a single dollar figure — with no visibility into which visits, procedures, or site fees it represents. Without line-level verification, sponsors pay for visits that never happened, procedures billed at off-contract rates, and duplicate charges, and they struggle to effectively forecast and understand how sites perform. 
• Reconcile by hand. Sponsors that receive details from CROs and payment vendors must match thousands of payment lines, in formats that rarely line up with your own records, against site contracts and visit data. It’s slow, and at that volume, discrepancies still slip through.

Every investigator grant has three versions of the truth: what the contract says should happen, what your trial data says happened, and what the vendor says happened. Our customers already had the first two in Condor. The vendor’s version was the missing piece.

Today, I’m excited to share that’s no longer the case thanks to AI-powered investigator grants intelligence.

AI-powered investigator grants intelligence

Now you can upload vendor payment reports, procedure data, and visit data directly into Condor. Built on the Biopharma R&D Graph, our clinical and financial knowledge graph, Condor’s AI normalizes each vendor’s format and maps every line to your EDC activity, protocols, and site contracts.

Once reconciled, Condor surfaces:

• Overbilling and pricing discrepancies. Wrong prices, off-contract charges, and duplicates, checked against contracted rates, amendments, and terms.
• Missing and unmatched payments. Payments with no activity behind them, sites paid with no subject enrolled, and completed work that hasn’t been invoiced yet.
• Patterns over time. Sites and vendors that consistently overbill, lagging invoices, and the split between startup and patient spend.

Every finding links back to the underlying data, viewable by site, visit, procedure, and site admin fee. And because payment history is now connected to trial performance, those patterns feed Condor’s forecasting. Your projected spend reflects how vendors actually bill, not just what they invoiced.

The result: your team stops taking the vendor’s number on faith and starts walking into vendor conversations with evidence.

Our promise: AI that takes on the work that slows you down

This launch is part of a commitment we’ve made to our customers. We’re building AI that automates the workflows that bog biopharma R&D teams down, so R&D teams can make clinical and financial decisions more quickly and confidently.

In September, we launched our Clinical Finance AI Agent, which explains the “why” behind a forecast variance and builds the resulting scenario in seconds. 

Five weeks later, we’re shipping investigator grants intelligence. Each launch follows the same pattern: find a workflow your team does by hand, understand it deeply, and build AI that runs it reliably, with every number traceable to its source.

What’s coming next

For investigator grants, the next step is moving from analysis on demand to continuous monitoring. Condor’s AI will watch payment and performance data as it arrives and flag anomalies the moment they appear. The site-level evidence it builds will also give you a stronger position when you negotiate CRO change orders.

Beyond grants, we have more AI agents coming in our product roadmap. I’m looking forward to sharing them soon.

If you’re a customer, connect with your customer success manager today to see AI-powered investigator grants intelligence in action on your own data. Not a Condor customer yet? Book a demo.

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Following closely on the heels of our $24M Series A funding round, today I’m excited to unveil our new Advanced Analytics and Dynamic Scenario Suite — a set of products that give R&D finance teams credible forward-looking intelligence that finance can actually defend in the boardroom, without sacrificing data integrity or waiting on Clinical Operations (ClinOps) to translate trial changes into financial impact. 

Biopharma companies can customize views and executive dashboards that are scenario-aware of their forecasting and planning, and traced back all the way to trial-level operational data. You can model enrollment delays or site activations without corrupting actuals, compare forecast versions side-by-side, and synthesize everything into live, shareable dashboards to equip your executive team and peers.

With the addition of the Advanced Analytics and Dynamic Scenario Suite, our AI-powered platform now covers the full R&D financial lifecycle, from automated accruals through forecasting and scenario planning, and all the way to company-wide and executive reporting. R&D teams now have one source of truth from close to strategy.

The suite represents the kind of constant innovation we're driving at Condor. And what we have up next on our product roadmap is even more compelling.

Our product vision is to unite ClinOps and Finance teams via a single source of clinical and financial truth, powered by AI. But not your standard AI. What we’re building at Condor is unique. 

ClinOps teams and Finance teams speak different languages. When a CFO needed answers that lived inside clinical operations, or when a clinical team needed to understand a budget decision that nobody had explained to them, the answer is usually to schedule a meeting, wait for the translation, and hope nothing got lost between functions. Meanwhile decisions are paused, programs slow, and surprises surfaced at board meetings. 

This isn't a people problem. It's a shared language and understanding problem. 

This problem can’t be solved by spreadsheets and services. Some organizations have realized that and are trying to solve the problem using AI. But the way most teams are implementing AI creates a new version of the same problem. You give a generic AI tool a set of rules, you feed it context, and you tell it how your trial works, how your CRO contracts are structured, and how your accruals map to your protocols. For a while, it works! Then the trial changes, the protocol gets amended, a new site opens, and a change order comes in. Now you have to rebuild the rules and feed the AI new context, which takes months. And then six months later, you do it all over again.

Clinical trials are not static. They are arguably the most change-intensive financial environments in any industry. Protocols change, enrollment shifts, and vendors renegotiate. Every change is a new context problem, and generic AI puts that burden on your team, every single time.

Condor is built differently because we started from a different premise. We didn't ask: How do we make AI work on top of your data? We asked: How do we build a system that already understands the relationships between your trial protocols, your site activity, your vendor contracts, and your accounting rules, and updates that understanding automatically as things change?

The answer is a knowledge graph built on a clinical and financial ontology that we developed over years with Big 4 accounting firms. Think of the ontology as the naming system — the semantic layer that defines what everything is and how it relates. The knowledge graph is the navigation system that knows how to move through that structure to find answers. When your protocol changes, the system updates the map. You don't rebuild it or feed it new context; It's already there.

What that unlocks isn't just faster R&D finance. It's something the industry has never had: a genuine translation layer between ClinOps and Finance. Data is unified, current, and explainable—not just to the finance team, but across the broader organization.

When ClinOps and Finance work from the same source of truth, programs move faster. Surprises get caught before they become crises. Go/no-go decisions get made on real data; not on someone's best guess stitched together the night before a board meeting.

Over the course of this year, we’ll introduce a series of AI agents fueled by our clinical and financial ontology and knowledge graph that bring our product vision to life. These agents will function like having multiple clinical finance subject matter experts embedded in your team, surfacing patterns, flagging risks, and bridging context across functions automatically. Not replacing human judgment, rather removing the laborious manual legwork that gets in the way of it.

Stay tuned for more info soon! And if you’re interested in piloting our agents, request a demo here!