AI agents purpose-built for biopharma R&D finance & ops
Condor AI Workflows is a team of AI agents that automate workflows — like forecasts, closes, accruals, and change order management — running them faster, more accurately, and more cost-effectively than manual analysis.
Leading biopharma R&D teams rely on Condor
The problem
Generic AI wasn’t purpose-built for the complexity of clinical trial finance.
R&D finance teams have hit a wall with generic AI tools, which aren’t built to reason over the complex, structured record systems their work depends on. Claude, ChatGPT, and Copilot produce numbers you can’t trust for three reasons:
Generic LLMs don’t understand your trial
Before you can understand the finances, you must understand the business. Generic AI doesn’t understand how your trial operates, your company’s business logic, or have the necessary domain context.
Generic LLMs hallucinate and drift
Generic AI has no source of truth, so it generates answers that sound confident but are factually wrong. And its outputs drift over time — the same question can get a different answer next month, even if nothing about your data changed.
Generic LLMs have no audit trail
Even when it gets the answer right, generic AI can’t explain how it got there. Without an audit trail, AI just produces answers — and if you can’t trace how it got there, you cannot validate it, defend it, or audit it.
The solution
Condor AI
Condor AI agents automate workflows — like forecasts, closes, and change order management — running them faster and more accurately and cost-effectively than manual analysis. Here’s why they produce trustworthy numbers:
Purpose-built ontology and knowledge graph.
Built with the Big 4 and grounded in how clinical trials actually work — protocols, sites, enrollment, vendor contracts, milestones. Condor AI reasons like a team member who intimately knows your business.
Separates the numbers and the interpretation of the numbers.
Your financial numbers — actuals, budget, forecasts — come from Condor’s deterministic financial engine, never from AI. Agents reason over those numbers: analyzing patterns, flagging anomalies, and explaining what’s driving them, but they never generate the numbers themselves.
AI doesn’t take action on its own.
Any material output like adjusting a forecast, calculating an accrual, and reconciling a balance requires human review and approval. It’s always clear what’s AI-generated and what’s system-calculated.
Security is built in.
Every customer’s data is isolated at the database level. AI models don’t have direct access to your data, and nothing is stored, reused, or used to train models. Everything is traceable, and every output can be audited.
A growing team of agents, not one generic assistant.
Forecasts, closes, accruals, change order management — each agent is purpose-built for a specific workflow, with more added over time.
Explains variance and cost-to-complete. The agent 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.
Runs scenario planning and builds the model. The agent 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.
Tuned to how FP&A and clinical ops teams actually ask questions. Powered by Condor’s proprietary knowledge graph and a deterministic math layer with AI reasoning on top, the agent is tuned to how FP&A and clinical operations teams actually ask questions; not generic corporate forecasting logic.
“Condor gives clinical finance teams a much more efficient and cost effective way to manage one of the largest costs and financial risk areas in the biotech space. If you're still working off manual spreadsheets, you're doing yourself a disservice.”
