AKASA Coding leverages generative AI and large language models fine-tuned on health system-specific data to automate and enhance medical coding and CDI processes. It reviews clinical documentation, suggests appropriate codes, provides justifications and direct evidence for recommendations, and flags missed revenue or quality opportunities. The platform is tailored to each client's workflows, boosting coder productivity, accuracy, and revenue capture while reducing administrative costs and error rates. It blends machine learning with expert-in-the-loop mechanisms for exception handling and continuous improvement.

Amy is an AI-powered medical coding solution that analyzes clinical notes to assign diagnosis and procedure codes with a claimed accuracy above 99%. It is designed to reduce cognitive workload, identify coding errors (including undercoding and overcoding), and improve coding turnaround time. Amy incorporates human-in-the-loop workflows, prompting for manual review when uncertain, and adapts to evolving medical coding guidelines through continuous learning.

Arintra's autonomous coding solution emphasizes support for primary care along with other clinical specialties. It automates the coding of patient charts from outpatient, inpatient, and urgent care settings, with a particular focus on high-volume environments like primary care and emergency departments. The platform integrates with EHRs to provide direct-to-billing codes without altering existing workflows, enabling primary care providers to reduce manual workload and improve accuracy in coding and reimbursement processes.