Visolyr is pleased to announce our acceptance into NVIDIA Inception, a program that nurtures startups revolutionizing industries with technological advancements.
✨ Generative AI to extract, summarize, and deliver clinical insights at the point of care.
⚙️ Autonomous Agents to drive intelligent, context-aware clinical and operational workflows.
🔄 Intelligent Automation to streamline processes, reduce burdens, and optimize .
Patient
Personalized and Accessible Care
Delivers faster, more personalized, and better-coordinated healthcare experiences.
Provider
Enhanced Clinical Efficiency
Automates documentation, prior auths, and data tasks, allowing clinicians to focus on patient care and reduce burnout.
Payer
Cost Reduction and Risk Management
Improves claims accuracy, lowers fraud, closes care gaps and enables smarter population health analytics.
Employer
Healthier, More Productive Workforce
Supports preventive care and wellness programs, reducing healthcare costs and boosting productivity.
Visolyr creates dynamic patient-specific documentation to include visit notes, care plans, discharge orders, post-op care instructions, referrals, prior authorizations and medication reconciliation summaries.
Visolyr utilizes NVIDIA® Inference Microservices (NIM)™ with TensorRT™ and TensorRT-LLM™pre-optimized inference engines to build upon non-supervised pre-trained LLMs and fine tunes the model with healthcare data to understand and generate domain-specific content while maintaining the general language and reasoning capabilities of the foundational model.
Visolyr combines information from multiple sources to generate concise synopses and surf actionable insights.
Visolyr streamlines repetitive tasks by generating structured content to prepopulate forms for referrals, prior authorizations and drug therapy enrollment.
Visolyr leverages NER to extract and classify entities with our generative AI content creation capabilities, resulting in more accurate and contextually relevant responses associated with patient information from disparate sources.
Domain-Specific Adaptation
Adapting pre-trained models to specific healthcare use cases to improve accuracy and relevance
Model Optimization
Fine-tuning models for better performance, faster inferencing and lower resource consumption
Multi-Modal Capabilities
Enhancing generative models to handle multiple types of input (e.g., text, image, video, and audio)
API-enabled Microservices
Ensuring seamless deployment of AI models across various cloud platforms or on-premises environments
Enterprise Integration
Integrating generative AI into legacy systems, CRMs, or ERP platforms
Multi-Cloud Support
Ensuring seamless deployment of AI models across various cloud platforms or on-premises environments
Conversational Interfaces
Designing AI-driven chatbots, voice assistants, or interactive systems for customer service or engagement
Personalization
Building systems that adapt AI outputs based on user preferences and/or previous interactions
Interactive Dashboards
Creating dashboards for visualizing AI outputs and monitoring model performance
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