We design evidence-led data systems and clinical AI for hospitals, providers, and health-tech teams — engineered for accuracy, audited for trust, and tuned to the patient in front of you.
Every model we ship has to earn its place in a workflow. We start from the bedside, the chart, and the constraints — then bring the math to meet them. Models arrive with their assumptions documented, their failure modes mapped, and a human in the loop wherever it matters.
Risk-stratification and recommendation engines built on graded evidence, with the reasoning surfaced so a clinician can agree, override, or audit at a glance.
Detection, segmentation, and triage models trained on DICOM-native pipelines, with drift monitoring for the populations you actually serve.
Early-warning signals on streaming vitals — sepsis, deterioration, readmission risk — delivered at the right place in the workflow with the right confidence interval.
Ambient documentation, multilingual patient education, and triage copilots — grounded in your sources, evaluated against clinical safety rubrics before they ever ship.
The unglamorous foundation: pipelines that handle HL7, FHIR, CDA, and X12 without silent data loss, with audit logs on every transformation and feature store tuned for the way clinical features actually behave.
Frontier and open-source large language models deployed safely inside your environment — grounded in clinical sources, evaluated against accuracy rubrics, and privacy-preserving by design.
Multi-step autonomous agents that navigate EHR workflows, coordinate care tasks, and handle prior-auth — with human-in-the-loop guardrails and a full audit trail on every action.
Domain-specific model adaptation on your clinical data — from efficient LoRA runs to full RLHF alignment — so the model reflects your protocols, your population, and your standards of care.
We build the connective tissue that keeps care moving — interoperable, compliant, and quietly fast. From EHR modernisation to revenue-cycle intelligence, our work fits inside what you already run.
Refactor legacy charting flows, extend Epic / Cerner / Athena with safe custom modules, or build a focused EHR for a specialty practice.
Device-agnostic RPM platforms, chronic-care workflows, and clinician dashboards that turn signal into action — without alert fatigue.
HL7v2, FHIR R4, CDA, X12, DICOM — engines, mappers, and conformance suites that let your data move where it’s needed.
Low-latency video, asynchronous messaging, e-prescribing, and scheduling — packaged for both new programs and existing networks.
Denial prediction, prior-authorisation copilots, and value-based-care analytics that translate the contract into a daily workflow.
Registries, screening programmes, and outcome dashboards built on de-identified data and reproducible cohort definitions.
Figures are drawn from anonymised engagements. Reference architectures and methodology notes available under NDA.
We sit with the data, the workflow, and the constraints — and write down what we think before we touch a line of code.
Architecture, model selection, evaluation rubric, and the UI — designed together so they don’t fight each other later.
Two-week cadences, demo every Friday, production environments behind your firewall or in ours.
Drift monitoring, periodic re-evaluation, and a clear path to hand the work back when you’re ready to own it.
All engagements operate under a BAA. PHI never leaves your environment unless explicitly authorised, and we use Safe-Harbor and Expert-Determination methods for de-identification with documented re-identification risk assessments.
Yes — we’ve shipped integrations with Epic (App Orchard / FHIR), Cerner, Athena, eClinicalWorks, and home-grown systems via HL7v2 and FHIR R4. We can also stand up an interoperability layer if you don’t have one.
Whichever is right for the problem. We fine-tune and train where data and risk justify it; we use frontier APIs (with proper data agreements) where they’re the better tool. Either way you get the evaluation evidence to back the choice.
A two-week Discovery, an 8–12 week pilot, and — if outcomes hold — a Pod engagement that scales the program. We commit to a no-fault exit at the end of each phase.
We publish methodology and tooling where it’s ours to publish, and we open-source the parts that benefit the broader ecosystem — never client data or differentiated IP. We’ll always tell you what we’d like to share before we share it.
Send the one paragraph you’d normally write to a colleague. We’ll reply within two working days with whether we think we can help — and what we’d do first if we could.