AUTOMATED RCM | End-to-End Revenue Cycle Management, Delivered White-Label
An end-to-end automated revenue cycle, built by Ajaia and delivered white-label under the client's brand
BACKGROUND
The client is a healthcare technology company that delivers revenue-cycle services under its own brand. Its operating workflow spans clinical documentation preparation, CPT coding, payer submission, and payment-status tracking.
THE OPPORTUNITY
Manual handoffs across each stage slowed claims, increased operating costs, and forced capacity to grow with headcount. Streamlining routine claims would allow the company to process more volume, resolve exceptions faster, and scale its white-label offering without proportional staffing growth.
OUR APPROACH
Ajaia built the revenue cycle as four automated stages with validation built into each one, then delivered the whole system white-label under the client's brand.
We started by tracing a claim end to end through the client's existing operation: where documentation sat, where coders queued, where submissions batched, and where payment status went unchecked. That produced a baseline of cost per claim, touches per claim, and time to submission, which set the targets the build had to beat and gave us a defensible before-and-after rather than an impression of improvement.
AI cleans and structures clinical documentation as it arrives, producing a codable record without a person reworking the note. Every downstream stage works from this cleaned record, so documentation quality is resolved once rather than repeatedly. Where a note is genuinely incomplete, the system routes it for clarification instead of coding around the gap.
The system assigns CPT codes from the cleaned documentation and applies consistent logic to every encounter. Because coding accuracy carries both revenue and compliance exposure, this stage was validated before it was trusted:
Certified coder benchmarking. System-assigned codes were scored against a reference set coded independently by certified human coders, measuring exact-match rate, undercoding, and overcoding separately. Undercoding leaves revenue on the table. Overcoding creates audit exposure. They are different failure modes and were tracked as such.
Documentation support testing. Every assigned code must be supported by specific language in the record. Codes the system could not tie back to documentation were treated as defects.
Confidence thresholds and routing. Claims below a defined confidence threshold route to a human coder rather than submitting automatically. The threshold was tuned with the client so straight-through rate rose only as accuracy held.
Regression testing. A standing suite of reference encounters runs on every logic or model change, so coding behavior cannot drift silently between releases.
Coded claims submit to payers as they are ready, with no batch window and no hand-worked portal session. The pipeline then follows each claim through to payment, monitoring status continuously and flagging only what needs attention. Scrubbing runs before submission so format and eligibility errors are caught upstream rather than returned as rejections days later. The client's team works exceptions; routine status checks never reach a person.
The pipeline ran in parallel with the client's existing workflow first, coding and tracking claims alongside human coders without submitting, so accuracy could be measured against live volume at no financial risk. Straight-through processing was then opened gradually by claim type, starting with the highest-confidence categories and expanding as accuracy held. Volume scaled only after the numbers earned it.
The entire pipeline runs under the client's own brand and presentation, with nothing in the product pointing back to a vendor. Ajaia built and maintains the engineering underneath. To anyone using the product, it is the client's claims platform.
THE SOLUTION
One automated revenue cycle under the client's brand: documentation in, coded and submitted claims out, payments tracked, exceptions surfaced.
RESULTS
A routine claim now moves from documentation to payment without human hands. Records are cleaned on arrival, coded without queueing, submitted without a batch run, and tracked without anyone opening a payer portal. Because every claim follows the same path, exceptions surface early and staff effort concentrates where judgment is actually required rather than being spread across claims that were never going to be a problem.
The operational effect is a cost structure that no longer scales with volume. Adding claims no longer means adding coders, which changes what the client can take on and what margin it holds when it does.
The business effect is an asset. The client now offers a fully automated revenue cycle under its own brand, built on engineering it did not have to staff for, that carries growing volume without proportional headcount.
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