Kythera Labs is building an AI-native healthcare strategy platform on Databricks that gives any health system access to the expert intelligence they need and can trust.

The meeting ends the way these meetings always end: with a question no one can answer quickly enough. A CEO, CIO, and CFO walk out of a planning session with a mandate: identify how much oncology revenue is leaving the system and where it's going. In a well-resourced health system, that question goes to a consulting firm and comes back six weeks and several hundred thousand dollars later. In most health systems, it goes to an analyst with a BI tool and comes back whenever it does, with whatever confidence the data allows.

The gap between those two experiences is the problem Kythera Labs was founded to close.

Health system executives face a complex set of strategic decisions simultaneously: growing patient volume, optimizing payer contracts, evaluating M&A targets, identifying underserved markets for expansion, and reducing administrative overhead, all with incomplete data. These decisions align with value creation levers, which historically have required expert analytical capacity that correlates almost entirely with institutional budget.

The analysts who can bridge that gap (who understand the missingness and bias in claims data, who can reconstruct a patient journey from fragmented billing records, who know the difference between what a claim says and what actually happened clinically) take years to develop. Large health systems hire them. Smaller organizations do without or spend millions on consulting firms to rent that expertise by the engagement. The strategic intelligence gap in American healthcare is not primarily a data problem. It's an expertise distribution problem.

Before any agent can answer a strategic question reliably, the data it reasons over has to be reliable. That's a harder problem than it sounds.