ABA Billing

What We're Testing

Building an AI-native billing and cashflow layer that learns payer-specific rules, flags denial risk before submission, automates documentation and follow-ups, and drives denials to resolution—so practices improve collections without building or outsourcing heavy billing operations.

The Problem and Why Now

Therapy providers lose meaningful revenue to manual, fragmented billing workflows and payer-specific requirements. Operators either hire expensive in-house teams or pay large percentages to third-party billers—yet claims still get missed, denied, or delayed, creating both revenue leakage and cashflow timing gaps.

Payer requirements continue to fragment while AI document processing and intelligent automation can now model payer-specific patterns, prevent avoidable denials pre-submission, and drive standardized resolution workflows. Behavioral health growth is creating thousands of new providers who need modern billing infrastructure from day one.

Our Hypothesis

If claim accuracy and denial resolution can be automated with payer-specific intelligence, then collections rise structurally and cash cycles compress—shifting billing from a labor-heavy cost center into software-driven operational leverage.

What Must Be True

That we can reliably increase net collections (and speed cash conversion) across multiple payers and states, while reducing manual touches per claim—without introducing compliance risk or degrading audit defensibility.

Who We're Learning From

Owners and operators of therapy practices and multi-location behavioral health providers managing complex billing across multiple payers and service types.

Focus Geography

Multi-state operations