Custom agentic revenue-cycle
management (RCM) solutions

Cost depends on scope, not on a fixed price list. The actual budget is shaped by the number of payers and specialties, integration depth, and compliance requirements. Because we assemble solutions from pre-built agentic components rather than coding from scratch, development costs are typically 30-40% lower than with traditional custom development. After a workflow analysis, we provide a fixed scope and budget, so there are no open-ended engagements.

With our component-based approach, delivery is 4-5× faster than traditional development. A single-workflow application (eligibility checks, prior authorization, or claim scrubbing) is typically live in 8-12 weeks. An AI automation middleware layer integrated with your existing systems takes 3-5 months. A full end-to-end RCM platform usually requires from 5-6 months to first production release, with functionality shipped in increments so you see working automation early, not at the end of the project. Timelines are confirmed after the initial workflow analysis, which itself takes 1-2 weeks.

We integrate through whatever interfaces your EHR exposes: FHIR and HL7 APIs where available, vendor-specific APIs (Epic, Oracle Health, athenahealth, eClinicalWorks, MEDITECH, Veradigm, NextGen Healthcare), and standard EDI transactions through clearinghouses such as Optum, Availity, and Waystar. Where APIs are incomplete, we handle the remaining steps directly, navigating portals or processing documents, so workflows still run end to end. Your data stays in the EHR as the system of record, our layer reads, normalizes, and writes back through controlled, auditable connections.

Front-end RCM covers everything before and at the point of care: patient registration and onboarding, insurance eligibility verification, benefits verification, prior authorization, and upfront patient cost estimates. Errors here are the root cause of most downstream denials. Back-end RCM covers everything after the encounter: charge capture, medical coding, claim scrubbing and submission, payment posting, denial management, appeals, and patient collections.

In two ways: prevention and resolution. On the prevention side, AI agents verify eligibility and benefits in real time, validate prior authorization requirements, and scrub claims against payer contracts and coding rules before submission, which is how our clients see up to 40% fewer denials. When denials do occur, the system categorizes them by reason code, identifies root causes, and routes routine cases into automated correction-and-resubmission workflows. Complex denials and edge coverage scenarios are escalated to your team through human-in-the-loop review, with full context attached: the original claim, payer response, relevant documentation, and a suggested appeal path. 

Yes. Multi-entity operation is an architectural decision we make at the start, not a feature bolted on later. The system supports separate payer contracts, fee schedules, coding rules, and workflows per specialty and per location, while keeping consolidated reporting and a single operational view. Each AI agent is configured to the payer rules and coding logic of the specific domain, whether that is dental, nutrition, behavioral health, or acute care, because effective agentic automation requires deep alignment with how each specialty actually bills.

Yes. The system generates and submits 837 claim files (professional, institutional, and dental as needed), processes 835 remittance advice for automated payment posting and denial capture, and supports the related transaction set: 270/271 for eligibility, 276/277 for claim status, and 278 for prior authorization. We integrate with major clearinghouses, or connect directly to payers.

Yes, both are core capabilities of our agentic components. For coding, the agent interprets clinical documentation and maps it to correct CPT and ICD-10 codes, then scrubs the coded claim against payer contracts before submission. For denial prediction, the system learns from your historical claims data and payer responses to flag high-risk claims before they go out, identifying issues such as missing authorization, coverage gaps, or documentation deficiencies.

Yes, both are core capabilities of our agentic components. For coding, the agent interprets clinical documentation and maps it to correct CPT and ICD-10 codes, then scrubs the coded claim against payer contracts before submission. For denial prediction, the system learns from your historical claims data and payer responses to flag high-risk claims before they go out, identifying issues such as missing authorization, coverage gaps, or documentation deficiencies.

Value-based care changes what the revenue cycle has to track: instead of only fee-for-service claims, the system must handle capitation, bundled payments, shared savings, and quality-linked reimbursement. We build this into the data model from the start, supporting attribution tracking, contract-specific payment logic, quality measure data capture, and reconciliation between expected and actual payments under each contract.

Yes, and it is designed to be cost-effective rather than a recurring drain. After launch, AI-assisted monitoring flags anomalies, tracks agent performance against baseline metrics, and automatically surfaces optimization opportunities. Because the system learns continuously from real workflows, decisions, and payer responses, much of the improvement happens without new development work. We also handle updates driven by external change: payer rule adjustments, coding updates (annual CPT and ICD-10 revisions), and regulatory shifts such as CMS-0057.