ContextRx gives healthcare teams the building blocks to build AI agents that understand the whole patient, follow real clinical evidence, and leave the decision to a clinician.
Decisions, not data.
A person's health story is scattered across claims, labs, pharmacies, and records that rarely talk to each other. When an AI agent acts on a fraction of that picture, patients get generic answers, missed drug interactions, and care gaps no one catches.
ContextRx assembles the whole person — what happened, and the behavioral why behind it — then turns it into a clear next step, with a clinician, not an algorithm, making the call.
Before a single agent ships, teams burn months wiring up data sources, negotiating content licenses, normalizing codes, and standing up consent and compliance — all before they write one line of clinical logic.
Everyone building healthcare AI runs into the same five problems — and none of them are about the model.
A patient's story is split across dozens of systems that never reconcile to one person.
The best clinical knowledge is copyrighted, and "free to read" rarely means "free to feed an AI." Some sources forbid it outright.
General models guess. A clinical decision can't be a guess.
LLMs are famously bad at the exact calculations clinical scoring depends on.
PHI, consent, and audit aren't things you bolt on at the end.
ContextRx packages the clinical knowledge, the clinical know-how, and the wiring into three composable blocks — available via MCP to power the agent you build and run. Take what you need, keep a human in the loop, and ship.
ContextRx is delivered the way modern agents expect to consume it — through the Model Context Protocol (MCP). Connect once to the platform, then turn on only the endpoints your agent needs, without building or maintaining a single data pipeline. Free, redistributable clinical sources are served directly; licensed content flows through your own licenses — so nothing in your stack is a legal surprise.
Look up clinical reference — terminology, drugs, coverage, guidelines — in a single call, no BAA.
Pull an identity-resolved, enriched member view, or a de-identified population comparison.
Invoke a clinician-approved skill — already wired to your context and its evidence.
The building blocks are the same. What you assemble — and the patient outcome you're chasing — depends on who you are.
Health systems, medical groups, and ACOs embedding clinical intelligence into care-team workflows and agents.
MA plans, PBMs, and risk-bearing organizations acting on Stars, HEDIS, and rising-risk before it escalates.
Pharma and biotech teams identifying cohorts and running adherence and safety programs on de-identified real-world data.
DTx companies grounding their app or agent in real patient eligibility, risk, and adherence — without building a data layer.
Every fact carries its source. Every source is checked against what it's actually licensed for — and anything that can't legally be used in AI simply isn't in the platform. PHI is handled in memory under a signed BAA, every access is audited, and every recommendation is built to end with a clinician's sign-off.
Tell us what you're building. We'll show you the knowledge base, context, and skills that get you there.