You were paying for seats. What you needed were answers.

If you've ever renewed a six-figure market-intelligence license and used a fraction of what you paid for, you already understand the shift underway in enterprise software — before the analysts gave it a name. For two decades, software was sold by the seat. A vendor's revenue grew with your headcount, on the assumption that a person sat behind every login, turning data into a decision. Agentic AI breaks that assumption. When a capable agent assembles in minutes an answer that once took an analyst a week, what you are buying is no longer a set of seats — it is the answer itself.

The market has started to reprice accordingly. This is not the end of software. It is the end of one pricing model, and of the six-figure contract built on it.

The change rewards some tools and exposes others. Exposed: commoditized dashboards and thin platforms that mostly route data to a person to interpret — the ones where you always suspected the annual price outran the value delivered. Strengthened: proprietary data, real domain expertise built into the system, and answers you can act on without hiring an analyst to translate them. The test, from the buyer's seat, is simple. Could you get this from an inexpensive, general tool already? Where you can't, the value is real. Where you can, you were overpaying.

Healthcare feels this first because the expensive part was never the software — it was the labor. Administrative and analytic work absorbs an estimated 30 to 40 percent of national health spending. The market intelligence behind a site, an acquisition, or a service-line decision has always existed, but it sat fragmented across claims, demographics, real estate, and provider data, assembled by analysts over weeks, one memo at a time. That is the cost the six-figure platform was really covering — and the cost agentic AI collapses.

Two things changed at once. The data rails became standardized and, increasingly, mandatory — federal interoperability rules now require modern, API-based access to health data, and national exchange networks are live. And the specialists who once did the assembly are scarce, with the projected shortfall running into the hundreds of thousands. The work still needs doing; there are fewer people to do it; and now there is a faster, less costly way to get it done.

Until now, the choice came down to two unsatisfying options. Buy raw data and build the expensive part yourself — the domain logic, the scoring, the interpretation that turn numbers into a decision. Or buy a subscription platform, priced by the seat, that you log into a few times a quarter. One hands you the ingredients; the other hands you a fixed menu and a six-figure bill.

The value was always in the middle — the domain layer that turns data into a decision. When every buyer rebuilds that layer alone, each one pays full price for it. Build it once and share it across everyone who needs it, and the cost of that expertise, per customer, falls toward nothing. Pair it with reasoning from the AI tools your team already uses, delivered into the workspace they already work in, and the finished answer costs a fraction of the old license. It is the same logic that made it cheaper to connect to a payments network than to build one.

This is the premise Catchment is built on. Not another dataset to license, and not another platform to log into — the healthcare market intelligence layer for the AI workspace your team already uses, with decades of domain expertise built into the answer and every claim traceable to the data behind it. You start with a single question, not a procurement cycle. Costs track the value you draw out — heavy during a live deal, quiet between them. The six-figure annual contract, signed in Q3 and live by Q1, stops being the price of admission.

The questions that move capital in healthcare — where to build, who to acquire, which market to enter, which service line to grow — were never worth a quarter and a six-figure license to answer. Increasingly, they won't cost one.