Antecedent Health builds the layer between a healthcare record and the questions people need to ask of it. The platform is built, it runs on real de-identified data today, and we are onboarding customers now — because the engineering underneath it is engineering nobody should have to build twice.
Across years of work on regulated and messy data — systems with no API, records that arrive as an export, PHI that must not reach a model, findings that have to be defensible — the same layer kept getting rebuilt: extract, de-identify, ground in a real source, govern what the system may decide alone, log everything.
At some point rebuilding it becomes negligent. So we built it once, properly, as a product — and pointed it at a domain where the stakes make the discipline obvious. Healthcare records are the hardest version of the problem: the data is sensitive, the rules change on a calendar, the authorities are documents rather than APIs, and a confident wrong answer has a cost. That product is Antecedent Health, and it's built to take on customers today.
We started at the claim, which is where the money is. An industry practitioner with decades in billing operations told us plainly that we had started in the wrong place: the foundation of this starts before the data set you are looking at. The code is derived from what was documented; the denial is derived from the code. Move upstream to the record and you can answer the billing question better — and you also unlock an entirely different question about treatment and outcomes. One correction produced two divisions.
It also produced the name. An antecedent is the thing that comes before and determines what follows. That is the whole argument: the record precedes the code, the code precedes the claim, and the treatment precedes the outcome. Start at the antecedent and both questions get easier.
Antecedent Health is built and run by engineers with a background building and operating cloud and data systems for organizations in regulated and operationally demanding industries.
Model access through Amazon Bedrock, so inference runs under enterprise data-handling terms from day one — not retrofitted onto a consumer stack.
Years of work moving and governing data out of systems that were never designed to give it up. Export-based ingest, normalization, and audit trails as a default rather than a feature.
We run production environments. That changes how you design a system that makes consequential findings: you assume you will be asked to explain one.
Because the fastest way to lose a serious audience is to overstate a stage they can verify.
This site exists to describe Antecedent Health accurately to the people we're talking to. If you want the detail behind any claim on it — the architecture, the corpus, the escalation rules, or exactly what is and is not built — ask, and we will show you the real thing rather than a slide about it.