We build deep learning systems, generative AI agents, and ML
infrastructure for hospitals, pharma, and medical device teams
from research prototype to compliant deployment.
Most clinical AI never reaches the patient. Models stall in notebooks, pilots can't pass IT review, and engineering teams aren't fluent in DICOM, FHIR, or the realities of a hospital network. We close that gap.
We take a working model from a paper, notebook, or lab andre-engineer it for real throughput, latency, and resilience.
HIPAA, GDPR, SOC 2, and HL7/FHIR aren't afterthoughts.We architect for audit and de-identification from day one.
Our engineers sit with radiologists, oncologists, and PIs. Theinterface fits the workflow not the other way around.
I am pleased with the team at RGT. Their coding skills, ability to handle challenges, experience with clients and engineers and competitive rates.
I am pleased with the team at RGT. Their coding skills, ability to handle challenges, experience with clients and engineers and competitive rates.
Segmentation, classification, detection, and predictive modelingacross imaging, signals, EHR, genomics, and multi-modal clinicaldata. From annotation pipelines to FDA-ready evaluation.
Clinical copilots, structured note extraction, multi-agentworkflows for ops and intake. Grounded, evaluated, and red-teamed for medical accuracy.
Scalable training and inference for longitudinal cohorts. MLOps,feature stores, model registries production hygiene for clinical-grade ML.
Full-stack product teams that ship: EHR integrations, clinicianportals, patient-facing apps. Ten-plus years of regulated-software craft.
Strategic consult, two-week engineering sprint, working demo. No retainer, noobligation.
Requirements, data audit, compliance plan,system design turned into an engineering roadmap.
Embedded squad ships incrementally. Clinical reviewers in the loop. Continuous evaluation against your golden dataset.
Production rollout, MLOps handoff, on-callSLAs. We stay until the model is yours to run.
We built an end-to-end triage model and clinician interface for a retinal imaging service handling thousands of cases per month. Deployed inside Mount Sinai's network, integrated with their PACS, and validated against radiologist consensus.
World-leading researchers in medical AI, ophthalmology, and computational
physiology guide our scientific direction and review the work that ships.
FIG. 01
Alon Harris
Vice Chair, International Research & Academic Affairs
Icahn School of Medicine at Mount Sinai
Ophthalmic AI · Ocular blood flow · Glaucoma
Internationally recognized clinical research scientist; Professor of Ophthalmology and of Artificial Intelligence & Human Health. Co-Director, Center for Ophthalmic Artificial Intelligence and Human Health. 419+ peer- reviewed papers; co-founder of the Society for Artificial Intelligence in Vision and Ophthalmology.
FIG. 02
Giovanna Guidoboni
Dean, Maine College of Engineering and Computing
University of Maine
Mathematical modeling · Data science for life sciences
Inaugural Dean of the Maine College of Engineering and Computing and Interim VP for Research at the University of Maine. AIMBE College of Fellows; member of the European Academy of Sciences and Arts. Research spans ocular blood flow, computational physiology, and non-invasive health monitoring.
Two weeks. A working prototype. No retainer.
