We start from how a hospital actually runs — the message streams already moving through it, the overnight coverage gap, the escalation path, the handoff at seven in the morning — and engineer the intelligence to fit that reality. Then we deploy it where your data already lives: your cloud, your servers, or a machine with no network at all.
The hardest problem in clinical AI was never the model. It's getting access to patient data to prove anything at all. We solved that by manufacturing our own.
Every capability is built and validated against synthetic patients with known answers. So we can show you the engine running on your message formats before a BAA, an IRB, or a security review is signed. Evaluation starts in week one, not month six.
Thirty days in shadow mode. No pages, no workflow change, and not one clinician gets another alert to ignore. You see what we would have caught before anyone has to trust it — which is the only order that has ever worked in this market.
On-premises or fully air-gapped. The most common reason clinical AI dies in security review simply doesn't apply — because the data never leaves the building. That is also what makes this deployable in the field, and outside the country the software was written in.
Failure to rescue — a deterioration knowable from data already sitting in the chart — drives extended ICU stays, transfers, and litigation exposure. The software was never the expensive part. The miss is.
Each of those removes a specific reason clinical AI deals die: data access, clinical risk, security veto, and unclear return. The defensibility isn't that the technology is clever — plenty of it is clever. It's that this one is provable before you commit to it, and almost nothing else in the category is.
We don't sell you a model. We make the ones you already own work inside clinical care — and we train them until a clinician will act on the output.
If your health system already licenses models, or owns weights you've invested in, the engine routes to them. Nothing about the clinical logic changes. The reasoning, the verification and the audit trail are ours; the model underneath is yours.
We train models against your terminology, your order sets, your escalation thresholds — and keep tuning until the output survives a clinician reading it at three in the morning. Generic models don't clear that bar. Trained ones do.
Your cloud, your servers, or a sealed appliance with no network connection at all. Data residency stops being a negotiation and becomes a deployment setting — which is what makes this workable outside the countries the software was written in.
Models are replaceable without re-engineering a single clinical rule. When something better ships — and it will, repeatedly — you move to it in a configuration change. The engine is built to outlive any one model generation.
Model-agnostic by architecture, not by marketing. We take no position on whose model is best this quarter, because that answer keeps changing and your hospital shouldn't have to re-platform every time it does. What stays constant is the clinical reasoning, the verification against the record, and the audit trail behind every claim. Your patient data never trains anyone's model — ours included.
Every source system names the same test its own way. The layer underneath resolves those names to shared concepts — and holds back anything it cannot resolve rather than guessing.
HL7 v2.x and FHIR R4 arrive with each system’s own codes, local mnemonics and units intact.
Structure, specimen and units are read as sent. Nothing is coerced to fit; conflicts are kept, not smoothed.
Each label is matched against the canonical dictionary and the loaded LOINC release. Only a release-verified exact match commits on its own.
Everything else becomes a proposal and waits for a person. Approval is a named, timestamped action.
Unverified bindings ship with the code omitted and a flag in its place, so a guess cannot be mistaken for a confirmation.
One trend line instead of three tests that looked unrelated. The two proposals still carry the local label they came from, so a reviewer sees what was actually sent.
has-unverified-loinc-binding in its place — a downstream server cannot mistake a guess for a confirmed codeCounted naively: three different tests, one result each, no trend. A measure computed over this is not wrong by a little.
One concept, three sources, one trajectory a specialist stack can actually reason over — with the local label preserved behind every point.
That source information becomes traceable clinical meaning — every resolved concept keeps the local label it came from, and every binding a machine could not verify waits for a person. LOINC and FHIR R4 are public standards, not CareCompile assets. The tiering rules, the canonical dictionary and the resolution logic are documented under NDA, not on this page. Nothing here is order entry: the layer decides what a result means, never what to do about it.
Forward-deployed means we work inside your environment, against your real data formats — not over the wall with a spec and a hand-off.
Map the workflow before writing anything — where the data originates, who acts on it, and what a miss actually costs the patient.
A working slice against your real message formats in weeks, not quarters. Real integration surfaces the problems a slide deck hides.
Every change is scored against a synthetic patient population with known answers, and a held-out evaluation harness scores each release against ground truth the engine never saw while it was being tuned.
Cloud, on-premises, or air-gapped. Same engine, same verification, same audit trail — wherever it has to run.
Findings are graded and routing is promoted or rolled back on evidence. Changes ship because they measured better, not because they sounded better.
// Nothing ships that the validation suite hasn't seen. Where this goes next — training on real clinical disagreement — let's talk.
Nocturnal On-call Reasoning Agent — always on, and deliberately quiet. It runs two loops, because patients deteriorate in two different ways.
The moment a result, note, or admission lands, specialist reasoning runs on that patient — labs, imaging, notes and prior history fused and read in parallel. Findings are queued the second they exist, and NORA works that queue continuously, through the night, without being asked.
Every five minutes, a sweep checks every admitted patient for what hasn't occurred — the results that stop coming, the monitoring that goes quiet, the admission nobody closed. Absence is a signal too, and it is the one that conventional alerting misses entirely.
Flagging is easy. Any system can flag everything — that is precisely how clinical alerting earned the reputation it has, and why the last generation of early-warning tools got silenced within months of go-live. The engineering is in what NORA decides not to say. What earns a clinician's attention at three in the morning, and what waits until rounds. Every decision is written to a tamper-evident audit trail — including the decision to stay quiet.
Every stack below reads the same patient record and the same knowledge base as NORA — nothing here works in isolation.
Point-of-care spine and neck assessment, validated under a U.S. Air Force Phase II program. A five-minute quantitative screen replaces a specialist referral for return-to-duty and conservative-management decisions.
Combat-medicine reference built for when no physician is on hand — voice-first, damage-control resuscitation, burns, blast trauma, and VA/DoD exposure doctrine, deployable fully offline.
Mass-casualty triage for a vessel with no doctor aboard. Reads a dropped photo or X-ray for a first-pass, non-diagnostic read, and compiles straight to a 9-line or SBAR handoff.
Whole-chart synthesis, spoken or templated document compilation, and every critical flag ranked in one view — the hub every other stack reports into.
Interpretation across hematology, microbiology, and transfusion medicine, scoped to LOINC, CAP, CLIA, and CLSI reference standards.
Modality-appropriateness guidance, RADS-structured reporting, and incidental-finding follow-through so a six-month recommendation is never dropped.
A single-lead capture, read in minutes — heart rate, rhythm classification, and an AI-assisted interpretation, the same hardware-to-software pipeline as SpineReady.
Infection and outbreak surveillance aligned to the CDC's Data Modernization Initiative — case reporting, resistance tracking, and cluster detection.
Turns medical, exposure, and movement signals into de-identified, analyst-reviewed intelligence drafts — never a diagnosis, never an unreviewed claim.
Interoperability engineering against CMS prior-authorization and payer-to-payer rules — evidence assembled before a claim is ever filed.
NORA, the always-on agent that watches every stack overnight, is covered in 04 — NORA above.
Every stack above reads and writes the same patient record and draws on the same clinical knowledge base — a fact that resolves for a physician, a fact that resolves for a field medic. Add a stack and it inherits everything the others already know. Nothing here is a silo.
Thirty days running alongside your live stream. We page no one. Nothing reaches a clinician, nothing changes a workflow, and no one gets one more alert to ignore. At the end we show you exactly what we would have caught, how early we would have caught it — and, just as importantly, everything we stayed quiet about.
Clinical Decision Support Notice — CareCompile is a non-diagnostic clinical decision support tool intended to augment, not replace, physician judgment. All AI-generated analyses are advisory only; clinical decisions remain the sole responsibility of the licensed treating clinician. CareCompile is not FDA-cleared or FDA-approved as a medical device and is not intended to diagnose, treat, cure, or prevent any disease. For investigational and decision-support use only. Validation with practicing physicians is ongoing.