Built for Clinical Impact. Engineered for Trust.
Domain-specific AI models for integration, prediction, and workflow precision in cardiometabolic care.
Domain specific RAG architecture
We build condition-aware retrieval pipelines that index structured and unstructured clinical data by patient ID, modality, and time. These pipelines power agents to surface guideline-aligned insights for conditions like diabetes and heart failure—ensuring traceable, context-rich outputs across workflows.
Outcome predictive LLM development
We train condition-specific LLMs on longitudinal EMR data to model risk in cardiometabolic care. These models forecast events like hospitalization or non-adherence and feed into triage logic and care escalation—continuously evaluated against clinical benchmarks.
Patient centered AI tools
Anora, our patient-facing agent, supports symptom reporting, medication guidance, and vitals tracking through multi-turn conversations. Built for accessibility, multilingual use, and edge deployment, it delivers structured updates to providers while preserving privacy. Patient interactions feed back into our models to improve relevance and personalization.
Forecast outcomes. Triage what matters.
Our custom LLMs are trained on longitudinal data to anticipate risk trajectories. By identifying early signals in vitals, labs, patient reported outcomes and population trends, these models guide escalation logic and recommend next-best actions—so care teams intervene earlier, with more confidence.
Free Up Time. Refocus on Care.
Let VascAI handle the small stuff — so you can focus on the clinical decisions that matter most.