98%
Multi-specialty Group (18 providers) — 19% denial rate, AR days at 52. → Denials 2.8%, AR days 14, manual work down 60%.
Trusted by startups and global leaders
The Full Revenue Cycle, Every Step Automated by AI Agents
An end-to-end platform fixes the handoffs. Data flows from scheduling through payment without manual re-entry, errors are caught before they propagate, and the AI layer sees the full claim journey rather than one phase of it.
Patient Scheduling & Pre-Registration
Capture insurance and demographic data upfront, flag visits needing prior auth, and get billing right before the encounter.
Insurance Eligibility & Benefits Verification
Real-time eligibility at scheduling and check-in — deductible, copay, coinsurance estimated accurately upfront.
Prior Authorization Management
Flag which procedures need authorization, pre-populate requests from clinical docs, and alert before expiration.
Charge Capture
Automated capture from clinical documentation with discrepancy flagging. Mobile entry fast enough staff use it.
Medical Coding & Documentation Review
AI-assisted ICD-10 and CPT suggestions, gaps flagged before audit, and provider queries that fix docs pre-claim.
Claims Scrubbing & Validation
Pre-submission validation against payer rules. Failed claims are held and routed for correction, not denied.
Claims Submission & Status Tracking
EDI 837 submission with real-time 277 status tracking, rejection management, and automated resubmission.
Denial Management & Appeals
ERA-based denial categorization by root cause, in-system appeal routing, and pattern reporting for the fix.
Payment Posting & Reconciliation
Automated ERA posting and EOB entry, with every payment compared to contracted rate — variances flagged.
Patient Collections & Financial Engagement
Cost estimates, statements, payment plans, and reminders calibrated to balance and history.
Contract Management & Payer Analytics
Payer terms maintained for real-time adjustment, plus analytics on underpayment and renegotiation.
6 of 12
Most Organizations Don't Have a Revenue Cycle Problem
They have six or seven smaller problems that together add up to one.
They have six or seven smaller ones that add up to one. Point solutions don't fix the handoffs between steps — where most leakage happens. An end-to-end platform fixes them.
Talk About Your RCM
End-to-End RCM Platforms We Built, Results and Case Studies
Each platform below replaced a revenue cycle that was leaking money. The numbers are measured in production, not projected in a pitch deck.
+22%
Oncology Practice — Auth gaps and incomplete drug-cost capture. → Auth denials gone, drug recovery up 22%, coding errors down 85%.
97%
Behavioral Health Network (8 locations) — Time-based coding errors. → Clean claim rate from 69% to 97%.
$180K
Ambulatory Surgery Center — Implant capture missing, underpayments untracked. → Implant recovery up 31%, $180K/quarter recovered.
-60%
DSO Dental Group (25 locations) — 25 billing operations, no group visibility. → Unified RCM, group denial rate down 60%.
100%
Home Health Agency — Documentation and billing disconnected. → Medicare compliance 100%, AR days 61 to 22.
Our Process
How We Build
We design the handoffs first and build the steps around them. Drag, click a card, or use the dots to walk the approach end to end.
We Start With Your Revenue Cycle Data
We start with your denial patterns, AR aging, and write-off history. The gaps tell us where the platform does the most work.
Every Payer's Rules Are Built In
Modifiers, auth policies, bundling edits, and filing limits — built in as maintained logic that updates when payer rules change.
The Handoffs Are the Priority
The cycle fails most at the transitions. We design the handoffs first and build the steps around them.
AI Trained On Your Data Before Launch
Denial, coding, and AR models trained on your historical claims during the build — ready at launch, not months after.
Integration With Your Clinical Systems Is Built In
EHR, scheduling, clearinghouse, and payer portals — built as first-class integrations. Manual data transfer anywhere in the cycle is a hole.
We've Built Across RCM Platforms
Every setting has its own payer mix, coding requirements, and revenue cycle failure points. Here's where we've built end-to-end platforms that collect more of what the clinical team earned.
Specialty-aware coding and payer rules
Denial rate cut from ~19% to under 3%
Complex chemo and drug-cost capture
Authorization gaps closed at ordering
Time-based coding accuracy
Payer documentation requirements met every time
Implant and supply cost capture
Underpayment detection and recovery
Unified RCM across all locations
Group-level revenue reporting
Visit documentation tied to billing
Medicare billing compliance built in
Enterprise claim volumes
Service-line revenue analytics
Sliding-scale and grant-aware billing
Medicaid requirements by state
Full cycle without a large billing team
AI prioritization of the AR that matters
The AI Layer Across Your Revenue Cycle
Across the full cycle, not just one step — spanning scheduling to collections. Hover a card to see what each model does.
Denial Prediction
Trained on your denial history, scoring every claim for denial risk before submission.
Coding Intelligence
Specialty-calibrated AI reads documentation and suggests codes at your payer mix's required specificity.
Authorization Intelligence
Current payer auth rules applied at ordering, flagging procedures and meds needing prior auth.
Underpayment Detection
Every payment matched against contracted rates, surfacing underpayments with documentation to pursue recovery.
AR Prioritization
Outstanding claims scored by recovery odds, urgency, and dollar value, so staff work top AR first.
Root Cause Analytics
Denials traced to upstream causes like scheduling gaps and documentation so fixes hit the real source.
95% → 100%
A Revenue Cycle That Works Shouldn't Feel Like an Achievement
Due to Poor Data Readiness.
Collecting ninety-five percent of what you earn takes constant effort. The five percent that doesn't get collected isn't lost because the care wasn't delivered — it's lost because the infrastructure wasn't built to protect it.
Book a Discovery Call
Compliance We Build To
Compliance is a design constraint wired in from day one, not a review step before launch.
Privacy
Privacy & Data Protection
Patient data protected across every region you operate in.
Security
Security, Risk & Payments
Independently audited security and PCI-compliant payment flows.
EDI & Claims
EDI & Claims Processing
X12 transaction set and billing rules built in as maintained logic.
Coding
Coding & Reimbursement
Coding and fee-schedule standards calibrated to your payer mix.
Regulatory
Regulatory & Transparency
Built to the price-transparency and patient-protection rules.
Fraud & Abuse
Fraud, Waste & Abuse
STARK and Anti-Kickback considerations built into billing logic.
Get in touch
Let's Talk About Your RCM Platform
Thirty minutes. No pitch. An honest discussion about where your revenue cycle is losing money and what closing those gaps would actually require.
Response within 24 hours
Your data is protected & secure
Free consultation, no obligation
Frequently Asked Questions
[ 1 ]
Which parts of the revenue cycle can AI agents automate today?
The highest ROI steps are the repetitive, rules-heavy ones: eligibility verification, prior authorization automation, medical coding, denial management, underpayment recovery, and cash application. Each runs as an agent with human review, so your team approves the work instead of doing it.
[ 2 ]
Do you replace our existing billing system or work alongside it?
Either. Most clients start by adding AI agents on top of their current billing system to automate specific steps, then expand. If you need the full platform, we build it end to end. All 32 of our production agents integrate with existing systems rather than forcing a rip and replace.
[ 3 ]
How is an end-to-end RCM platform different from point billing software?
Point software automates one or two steps. An end-to-end platform connects every step in one system where data flows without re-entry, errors are caught before they propagate, and the AI sees the full claim journey. The difference shows up in the handoffs — where most leakage happens.
[ 4 ]
How long does implementation take?
A single-specialty platform typically runs five to eight months. An enterprise platform across multiple specialties, locations, and complex payers with full AI runs ten to sixteen months. You get a milestone-based timeline after discovery.
[ 5 ]
What happens to our existing billing data?
Migration scope — claims, AR aging, payment history, patient balances — is defined during discovery. We tell you what migrates cleanly, what needs transformation, and what the parallel operation looks like. Nothing is discovered at go-live.
[ 6 ]
How does the AI get trained on our data?
We extract two to three years of historical claims with outcomes during the build. Denial prediction, coding, and AR prioritization models are trained and validated before launch, and keep improving on live data.
[ 7 ]
Can the platform handle multiple specialties with different billing requirements?
Yes — and this is where custom builds beat commercial options. We configure specialty-specific coding, payer rules, and documentation requirements within one unified platform rather than separate systems that don't share data.
[ 8 ]
Who owns the platform?
You do. Full IP transfer at project close — source code, documentation, trained model artifacts. No per-claim fees, no licensing that scales with your billing volume.
Global presence
