Visolyr provides a clinical AI platform that continuously analyzes longitudinal EMR data, diagnostics, unstructured notes, and social determinants to generate real‑time, patient‑specific insights and evidence‑based recommendations within the clinician’s workflow. By automating documentation, care plan creation, and prior authorizations, it reduces cognitive load for providers and enables proactive prevention and personalized treatment of cardiometabolic disease, improving outcomes and lowering costs for health systems.
Funding
Funding not disclosed
Founders
Product
Problem
Uncontrolled cardiometabolic disease leads to frequent complications, high acute‑care utilization, and escalating costs for patients, clinicians, and health systems. Existing clinical workflows lack real‑time, patient‑specific insights that can guide proactive prevention and personalized treatment.
Solution
Visolyr offers a clinical AI platform that integrates longitudinal electronic medical records, diagnostic results, unstructured notes, external data sources, and social determinants of health. Its Agentic Adaptive Intelligence™ continuously interprets each patient’s data, formulates context‑aware questions, and generates evidence‑based recommendations that are delivered directly within the clinician’s workflow. The platform automates clinical documentation, produces concise patient snapshots, and creates personalized care plans, referrals, and prior authorizations. By providing diagnostic assistance, tailored treatment suggestions, and optimized care pathways, Visolyr enables providers to intervene earlier, reduce cognitive load, and improve outcomes for cardiometabolic patients.
Target Audience
Primary customers are hospitals, health systems, and integrated delivery networks that manage large cardiometabolic patient populations, as well as physicians and care teams seeking AI‑augmented decision support within their electronic health record workflows.
Features
- Autonomous agents that synthesize structured and unstructured EMR data to produce real‑time patient snapshots
- Context‑aware, intent‑driven recommendations for diagnostics, treatments, and care plans based on genetics, biomarkers, lifestyle, and social determinants
- Automated generation of referrals and prior authorizations, reducing administrative burden and ensuring clinical accuracy
- Integration of NVIDIA NIM microservices and domain‑optimized foundation models (e.g., Llama Nemotron, Palmyra‑Med‑70B) for low‑latency inference across cloud, edge, or on‑premise environments
- Seamless embedding of AI insights into existing clinical workflows and EHR interfaces without disrupting provider routines
- Continuous learning loop that adapts recommendations as new patient data become available, supporting proactive disease management