Know what a claim pays before you send it.
Medicare rules replayed, commercial payers learned from their own history, and denial causes caught before submission.
The payer pays $172. The remittance lands inside the predicted range.
Claim 4471, priced before it goes out. 1. Claim 4471 arrives as a standard 837P. Nothing new to produce. 2. Bundling, units and diagnosis validity are checked before it goes out. 3. The line is priced on the fee schedule in effect that day, and against this payer’s history. 4. Expected payment $172, range $158–$186, denial risk 2.4%. An estimate, not payer approval. 5. The payer pays $172. The remittance lands inside the predicted range. Illustrative, synthetic data.
Prediction figures were measured out-of-fold on one urgent-care provider’s ~5-year de-identified claims history (17,463 insurance lines); the model calibrates to each provider’s own payers over time, so they are not a guaranteed rate. Grouper throughput: 20,000 claims on a single 2-vCPU container, zero errors.
The answer, before the money is at risk.
Payers answer weeks after submission. The engine answers before.
Reads the files you already produce.
Standard X12 files, or an API and SDK beside your EHR.
Catches denial causes.
Bundling, unit limits, diagnosis validity and lab necessity, checked before sending.
Every line, priced.
Professional, lab, outpatient and inpatient, on the fee schedules in effect that day.
A predicted EOB, line by line.
Expected payment with a range and denial risk. An estimate, never payer approval.
Predicted payment
Denial risk
Sharper with every remittance.
Predictions recalibrate to your payers. Learned edits wait for a person’s approval.
Rules replayed. Payers learned.
Priced from published rules.
Fee schedules, national edits and the MS-DRG grouper. Exact from the first claim.
Predicted from their history.
Learned from each payer’s remittances. Too little history means rules only, no percentage.
Where your billers work.
Predicted payment
Denial risk
One engine for the whole claim.
Inpatient DRG
The CMS MS-DRG grouper, versioned by discharge date, priced by IPPS.
Outpatient pricing
OPPS and APC rates across 19,000+ codes.
Professional + facility
Both claim types, with the right unit limits applied.
Denial firewall
Bundling, units, code validity and lab necessity, checked pre-submission.
Silent-claim worklist
Unanswered claims surface oldest first, with follow-up drafted.
Embedded or standalone
Inside VAERION or beside your EHR, with an append-only audit trail.
Everything after the claim leaves.
On the same record, with a person on every decision that moves money.
Acknowledgments
Matched to each claim. Quiet claims flagged before they age out.
Corrections & deadlines
Linked replacements and voids, timely-filing dates, and a per-payer send stop.
Remittance posting
Balanced payments post automatically. Everything else waits for review.
Contracts & underpayments
Each line compared to the contract, with every variance explained.
Appeals
Drafted only from facts on file, with gap markers and deadlines.
Secondary claims
Primacy rules applied, drafted with the primary’s actual payment.
Coverage discovery
Finds insurance for self-pay patients and re-checks upcoming visits nightly.
Patient estimates
Good Faith and insured estimates from contract and verified benefits.
Statements & cashiering
30/60/90-day portal statements, cash drawers, day sheets and reasoned voids.
Credits & refunds
Overpayments detected, the 60-day window tracked, refunds double-approved.
Prepayment integrity
Eleven cited rules, including OIG exclusion checks. Overrides need a reason.
Revenue reporting
48 reports, from clean-claim rate to predicted versus actual, exported or scheduled.
Measured, with the method attached.
Medicare-exact · ~7,800 claims/sec
p95 latency 4.2 ms. Zero errors across 20,000 claims. Fully deterministic.
95–97% range coverage · ≈$49 / line
99–100% at plan level on high-volume codes.
0.4% calibration error
Predicted risk tracked observed denials within 0.43% across risk levels.
Prediction figures were measured out-of-fold on one urgent-care provider’s ~5-year de-identified claims history (17,463 insurance lines); the model calibrates to each provider’s own payers over time, so they are not a guaranteed rate. Grouper throughput: 20,000 claims on a single 2-vCPU container, zero errors.
Grounded in official CMS data.
1.7M+ official edits
National NCCI and MUE tables, applied automatically.
Medicare fee schedules
Physician, lab, outpatient and inpatient.
The MS-DRG grouper
The reference grouper, versioned by discharge date.
Diagnosis & necessity rules
ICD-10-CM validity by date, and national lab coverage rules.
What’s live today.
Every capability, labelled plainly.
- Live
- In production today.
Buyer questions.
How is this different from a clearinghouse?
How accurate is it?
Is the DRG assignment defensible?
Commercial payers too?
Do we replace our EHR?
Can it run on our PHI?
See your predicted revenue.
Bring a de-identified claims sample. See what each line should pay.