Baseline Synthetic's founder built and led the analytics functions that govern how a top-five US pharmaceutical company commercializes its products — how patients are tracked through treatment, how channel and payer performance is measured, and how billions of dollars in revenue are recognized and optimized. The structural efficiency that defined that work is the model for what Baseline Synthetic delivers at industry scale.
As Vice President of Data Science, he led the global commercial data science function incubating the company's next generation of AI/ML capabilities — analytics chatbots spanning primary and secondary data, agent-driven patient funnel diagnostics, and AI-powered alert systems built on detailed claims data. As Senior Director of Commercial Analytics, he led the reorganization of the global analytics services function, delivering more than 50% cost savings while improving analytical quality and insourcing high-value innovation.
Baseline Synthetic is built on a precise observation: the AI capabilities that now exist are sufficient to perform the analytical work the founder has led across two decades. The bottleneck has never been model capability — it has been the domain expertise and data architecture required to deploy AI correctly in a domain with this much regulatory complexity, data fragility, and financial consequence. That bottleneck is what Baseline Synthetic removes.
The convergence of model capability, agent frameworks, and cloud-scale data infrastructure that is transforming knowledge work broadly is — with the right domain expertise built in — sufficient to run the majority of pharma commercial analytics and operations functions autonomously. The bottleneck has never been the AI. It has been the expertise required to deploy it correctly in a domain with this much regulatory complexity, data fragility, and financial consequence.
The advantage of starting from inside the function is twofold: the semantic data model gets designed by someone who understands the business meaning of every field, not just its structure; and the model gets maintained at the velocity the business actually changes — not on a quarterly software release cycle. That is the velocity an entrenched data vendor cannot match.
See what we're building →