An AI system that spots the money your hospital is about to lose on billing errors and fixes it before the check bounces.

Eos AI deploys supervised and semi-supervised machine learning models trained on historical claims data, denial patterns, payer behavior, and clinical documentation quality to predict which claims are at high risk of denial, underpayment, or delayed reimbursement. The system continuously ingests data from EHRs, billing platforms, and payer portals, building a real-time risk score for each claim in the pipeline. When a claim crosses a configurable risk threshold, the platform triggers automated remediation workflows—such as documentation enrichment, coding correction, or escalation to a human reviewer—before submission. Over time, the models retrain on outcomes to improve precision, creating a closed-loop system that adapts to changing payer rules and institutional coding patterns. This approach shifts revenue cycle management from reactive (appealing denials after the fact) to proactive (preventing denials before they occur), which is where the claimed 37% revenue recovery improvement originates.

It's like having a spell-checker for your hospital's invoices that fixes the typos before you hit send, so the insurance company can't send it back.

An AI librarian that reads every doctor's messy handwriting across every hospital system and organizes it all into one clean, searchable file.

Eos AI uses large language models fine-tuned on clinical corpora to parse unstructured clinical notes, radiology reports, pathology results, and discharge summaries across multiple EHR systems. The platform applies named entity recognition (NER), relation extraction, and temporal reasoning to convert free-text into structured, coded data elements (ICD-10, SNOMED CT, LOINC). A federated indexing layer then maps these structured elements across distributed data sources—without physically moving the data—creating a virtual unified patient record that clinicians and administrators can query as if it were a single database. This eliminates the need for costly data warehousing or manual chart abstraction, and enables downstream analytics (population health, quality reporting, risk adjustment) to operate on complete, harmonized data. The system continuously learns from clinician corrections and feedback, improving extraction accuracy over time and adapting to institution-specific terminology and documentation styles.

It's like Google Search, but instead of the internet, it searches every scribbled note and scan across your entire hospital network and gives you one clean answer.

An AI assistant that fills out all the insurance paperwork for your doctor's office, submits it, and follows up until it's approved—so humans don't have to.

Eos AI's agentic workflow engine orchestrates multi-step prior authorization processes end-to-end. When a clinician orders a procedure or medication requiring prior auth, the system automatically identifies the payer's specific requirements, queries the unified patient record for relevant clinical evidence (diagnoses, lab results, imaging, prior treatments), populates the authorization form with structured and unstructured supporting documentation, and submits it electronically to the payer. The agent then monitors the submission status, responds to payer information requests by pulling additional evidence from the patient record, and escalates to a human reviewer only when the case falls outside its confidence threshold. The system uses reinforcement learning from historical auth outcomes to optimize evidence selection and submission strategies for each payer, learning which documentation patterns lead to fastest approvals. This closed-loop, adaptive approach transforms prior auth from a labor-intensive, multi-day manual process into a largely autonomous, same-day workflow—directly addressing one of healthcare's most universally despised administrative burdens.

It's like having a tireless intern who actually enjoys filling out insurance forms, never forgets a document, and follows up with the insurance company so you don't have to sit on hold.