PhareOS: AI Built for Complex Revenue Workflows
Each patient encounter must be translated into the exact language insurers require. When processes run on fragmented systems and manual handoffs, meaning gets lost and delays follow.
Our Revenue Operating System unifies processes, reading the full record, applying payer rules and ensuring accurate translation.
Build AI that makes an impact
Massive datasets, regulated environments and model explainability tradeoffs, deployed across the top health systems. Your work will ship, scale and improve care delivery nationwide.
97%+
autonomous coding accuracy processing full EHR datasets on our LLM-based engine
180M+
payer transactions on an AI infrastructure powering the future of healthcare
1,000+
payers, 30+ systems and trillions of tokens connect on our AI-native platform
Startup speed. Enterprise impact. Real results.
Evolve the revenue cycle to be AI-native.
Change how healthcare gets paid.
Build enterprise-level machine learning pipelines, agentic workflows, payer intelligence engines and autonomous follow-up systems. Deploy secure, compliant AI systems across enterprise healthcare networks. Tackle challenges across scalability, latency, reliability, security and production.
The Cultural and Engineering Behind Our AI Systems
Strategic growth. Technology excellence. Engineering collaboration.
The same principles that guide our frontline teams guide our engineers: ownership, clarity, continuous improvement and measurable impact. We embrace challenges because that’s how professionals grow. We pursue excellence because building AI for healthcare requires trust, clarity and shared accountability.
Partner with Purpose
Build systems that directly affect hospitals, clinicians and patients. Contribute through code quality, compliance guardrails and model explainability. Impact is measured not just in metrics, but in real outcomes for care providers.
Think Boldy
Challenge legacy workflows and redesign them as intelligent systems. From LLM-based reasoning over long documents to real-time evaluation pipelines and AI governance frameworks, experiment deliberately, then ship with discipline.
Go Beyond
Enterprise AI requires resilience. Take control of system reliability, model drift, monitoring, performance tradeoffs and scalability. Debug production issues, refine evaluation datasets and continuously improve systems deployed across leading health systems.