platoseedplatoseed
Autonomous OS for healthcare
Eos helps clinics turn their data into actionable insights to identify more eligible patients and early care interventions. We harmonize historical data across all systems to create standardized patient data distributions, acting like a translation layer between different applications. Then we build a centralized index over it: a compressed representation that lets petabytes of raw data stay where it is while allowing us to search and reason across it as one system. Instead of treating the visit or document as an isolated instance, we can look at it as a continuous story. We can predict how a case will resolve, trigger the required approvals and actions, and do so while reducing staff workload and recovering hospital revenue.
Eos AI positions itself as an Autonomous Operating System for Healthcare that harmonizes data across disparate systems and accelerates discovery. It focuses on unifying silos, standardizing data, and deploying predictive models with workflow automations to improve productivity and revenue recovery.
Eos AI connects to multiple healthcare data interfaces (EHRs, imaging archives, labs, scheduling, and billing) to resolve identities and link records into a continuous longitudinal history, while the data remains in its original systems. It standardizes codes, imaging metadata, measurements, and documentation to create consistent patient trajectories. Key components include VERA for imaging harmonization, LUCIA for textual harmonization of EHRs, predictive models that use full patient histories to forecast case resolutions and trigger actions, and automations that integrate insights into hospital workflows to automate prompts and coordination tasks.
Who it’s for: Healthcare providers and systems seeking to unify disparate data sources, improve model performance, and automate workflows to recover revenue and reduce staff workload.
Reference to deployments and model performance improvements; presence of a demo booking call-to-action suggests active customer engagement and early traction, though explicit customers or funding details are not listed.
CS + Math @ Caltech | Research @ Stanford Medicine | YC Summer Fellow
Our healthcare system is failing the people it’s supposed to serve. At Eos AI, we are fixing that.
Eos AI acts as an intelligent hub for healthcare data, harmonizing fragmented EHRs and systems into a unified, searchable representation to enable predictions across a patient’s history. It targets hospitals and clinics, aiming to trigger approvals and actions to reduce staff workload and recover revenue, with early pilots showing improved admin productivity and revenue recovery.
AI Employees for Primary Care
AI-native patient engagement
