CEOs, CFOs, COOs, RCM leaders, payer/provider operators, and healthcare AI founders

AI Solutions Design and RCM Orchestration

High-level AI solution design for healthcare revenue cycle, prior authorization, claims intelligence, payer/provider workflow, and governed agentic orchestration.

When this work matters

Leadership knows AI will touch revenue cycle, prior authorization, claims, payer portals, access, and RevOps, but needs the architecture, governance model, and proof loop before buying or building tools.

Founder problem

The organization is surrounded by AI point solutions, but the work still lives across payer rules, portals, EHR data, CRM, RCM queues, documentation, humans, and exceptions. Without a design layer, automation can create more fragmentation.

What gets built

Solution architecture across signal intake, context assembly, agentic work blocks, human review, system action, and proof loops.
RCM and denial-prevention design patterns for eligibility, documentation, coding review, authorization packets, appeals, and underpayment triage.
Prior authorization orchestration model for APIs, portals, payer rules, evidence packets, status monitoring, and escalation.
Governance model that defines where AI can recommend, draft, route, monitor, or score, and where human approval is required.
Measurement spine for cycle time, clean-claim rate, denial prevention, appeal yield, A/R days, access speed, and staff capacity.

Proof patterns

$125.9M claims/referral leakage surfaced through claims forensics.
AI-assisted RevOps patterns across lifecycle architecture, scoring, attribution, and revenue-quality reporting.
Payer/VBC and provider-network operating fluency across reimbursement, workflow, access, and value proof.

What not to do

Do not start with a model demo before mapping work ownership and evidence sources.
Do not let agents make autonomous clinical, referral, payer, or patient-impact decisions without human review.
Do not measure AI value in prompts, documents generated, or dashboard views alone.

Decision questions

Which revenue-cycle or access workflow is costly enough and bounded enough to design first?
What evidence sources, rules, exceptions, and approvals must be assembled before action?
Where can AI safely draft, score, route, or monitor without replacing human judgment?
Which proof metric would make the CFO, COO, or founder believe the system is working?

Related proof

$125.9M leakage surfaced through claims forensics

Databricks Claims Forensics / Referral Leakage

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1,200+ clinicians onboarded in 90 days

Behavioral Health Growth / AI RevOps

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$13M+ annualized referral revenue across 13 facilities

Dialysis Network Growth

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Build the wedge. Prove the motion. Scale what repeats.

For Series A/B teams that need sales, partnerships, implementation, payer logic, and revenue intelligence to become one operating system.

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