Revenue · RCM & Prior Authorization
Denial Management AI for Medical Billing
Score claims before submission and fix the ones likely to bounce before days in A/R pile up.
Primary search: denial management AI
The problem
Denial work is reactive. Billers fight fires after the claim drops. First-pass yield stays stuck.
Ops-only RCM AI: denials, packets, policy RAG. No diagnostic claims. Faster ROI, lower liability surface.
Buyer: VP RCM or Billing Director at a DSO, specialty group, or medical billing company
How we ship it
- 01Score denial risk pre-submission
- 02Route high-risk claims for human fix
- 03Learn from payer-specific reject patterns
- 04Measure first-pass yield and A/R days
Outcome
Prevent denials before they become work.
KPI: Revenue
How to engage
Start bounded on this workflow. Expand only after the KPI moves.
Denial / prior-auth diagnostic
From $7,500Baseline first-pass yield and packet cycle time; pick the first mandate.
Scoped production mandate
From $15kDenial scoring or prior-auth packets live in 6–10 weeks.
Embedded partnership
From $25k/moExpand payers and specialties with your RCM team.
Related searches this page covers
- denial management AI
- AI for revenue cycle management
- AI consulting for medical billing companies
Frequently asked questions
How long does it take to ship denial prediction?
Most denial prediction mandates target a 6–10 week production deploy after a short diagnostic. Week one locks the KPI (revenue) and data sources; we redirect if a simpler path wins.
Do we have to replace our existing systems?
No. We orchestrate against the systems you already run. The first mandate is denial management AI in your stack, not a platform rip-and-replace.
How is this different from buying another SaaS tool for RCM & Prior Authorization?
You own the code, evals, and models in your cloud. SaaS adds another login; we ship a production workflow your team operates with the Production Standard contracted in writing.
Is this clinical AI?
No. We do not diagnose. We automate billing ops: denials, packets, and policy retrieval. Lower regulatory surface, faster ROI.
How do you scope AI for our industry?
Week-one discovery locks business KPIs (revenue, cost, risk, or cycle time), not model vanity metrics. We map your data sources, compliance constraints, and existing stack, then propose a fixed-scope mandate with acceptance criteria or redirect you to a simpler path.
Do you have experience in our sector?
Proven depth sectors (financial services, federal, real estate, telecom, higher education) reflect production systems we've shipped. Growth and operator verticals (MGAs, credit unions, property management, 3PL, RCM, staffing, licensed iGaming, FFL) are selective mandates where those patterns transfer with industry-specific tuning. Biotech, insurance, and regulated work often runs under NDA.
The Production Standard
The Production Standard
Six commitments contracted in writing on every engagement.
01
Deploy or redirect
If AI isn't the right tool, we say so in week one and redirect budget to what will ship. No POC theater.
02
Metrics before models
Business KPIs locked in discovery. Model selection follows the metric, not the hype cycle.
03
Governance from day one
Model cards, access controls, evaluation harnesses, and audit trails, not a compliance bolt-on at go-live.
04
Production artifacts
Deployed code, monitoring dashboards, CI/CD pipelines, and incident runbooks. Not recommendations for someone else to implement.
05
6-week production target
Scoped projects designed for production in 6–10 weeks. Boutiques move; global programs wait for steering committees.
06
Capability transfer
Your team can operate, extend, and maintain what we build. IP and documentation transfer is included, not upsold.
Every scoped project includes a production deliverable checklist signed off before close. If we can't commit to deployable output, we won't take the engagement.
Selective intake · 2026
Your next system shouldship to production.
Agentic development from architecture through deployment, with the Production Standard contracted in writing.