Radiant Network
Prototype · Investor Demo Ready

The AI-native operating layer
for modern radiology networks

Workforce orchestration, multi-state licensing intelligence, and AI-assisted revenue cycle — unified into a single operational layer so radiology networks can scale from study intake to paid claim.

Request a Shadow Run radiantnetwork@proton.me
2–4 hrs
Avg. STAT delay today
manual routing
~12%
Radiology coding error rate
industry average
40+
IMLC compact states
one license, multi-state
$20.2B
Radiology practice revenue
US market, 2024
Platform

Three pillars. One operational layer.

The pieces that make radiology networks work — orchestration, compliance, and clinical AI — finally connected in real time.

Workforce Orchestration
Right radiologist. Right study. Right now.
Real-time routing across your credentialed radiologist network — filtered by state license, subspecialty, malpractice status, and live workload. STAT studies are never manually assigned.
  • Multi-state IMLC routing
  • Subspecialty matching
  • Workload balancing
  • Credentialing status checks
Licensing Intelligence
Coverage gaps caught before they become compliance gaps.
Continuous monitoring of multi-state license status, renewal timelines, and malpractice coverage across your entire radiologist network. What-if modeling shows exposure before it becomes a problem.
  • IMLC compact tracking
  • Renewal alert system
  • Coverage risk scoring
  • What-if scenario modeling
AI-Assisted Workflow
Decision support, not autonomous diagnosis.
Claude Sonnet provides radiologists with structured findings and pattern recognition as a consultation layer — not a replacement. Auto-coding and claim submission complete the loop from read to reimbursement.
  • Claude Sonnet decision support
  • AI-radiologist consensus workflow
  • Critical finding escalation & referring MD alerts
  • Auto CPT / ICD-10 coding
  • Claim submission & tracking
Why now

The problem is fragmentation.
We are the connective layer.

Credentialing, licensing, routing, reading, coding, and billing each live in separate systems — none talk to each other in real time. The result is manual coordination at every step, compliance exposure from untracked license lapses, and diagnostic AI tools that operate in isolation with no workflow integration.

The Problem
STAT studies wait for manual radiologist assignment
License lapses go undetected until a claim is denied
Critical findings aren't reliably escalated to referring physicians
Diagnostic AI flags sit in dashboards — no operational follow-through
Billing coding is manual, slow, and error-prone
Our Position
Rad AI, Aidoc, Viz.ai
Detecting findings in images
RIS / PACS vendors
Study storage and basic workflow
Credentialing software
Static license management
Radiant Network
Operational intelligence connecting all of it
We are not competing with diagnostic AI companies — we are the operational layer that makes all of them more useful. Diagnostic AI companies often lack workflow infrastructure. Operational software often lacks modern AI orchestration. Hospitals hate fragmented systems. Radiant Network converges all three.

Tool fatigue is real.

Radiologists today manage 5–10 disconnected AI tools. Radiant Network is the single orchestration layer that makes all of them work together — one worklist, one workflow, one loop from study to reimbursement.

Integration

Your existing diagnostic AI
investment doesn't go to waste.

Radiant Network imports findings from Rad AI, Aidoc, Viz.ai, and other vendors via DICOM SR and HL7 FHIR. Their finding becomes our trigger.

Third-Party AI Integration DICOM SR · HL7 FHIR
Diagnostic AI
Rad AI · Aidoc · Viz.ai
›
DICOM SR Import
HL7 FHIR · structured
›
Radiant Routing
IMLC · subspecialty
›
Physician Sign-Off
Review · consensus
›
Claim Submitted
CPT · ICD-10 · clearinghouse
Rad AI
Triage & prioritization
Aidoc
Clinical AI platform
Viz.ai
Care coordination AI
"Without Radiant Network, a Rad AI triage flag sits in a dashboard. With it, that flag triggers credentialed routing, escalation, physician sign-off, and a submitted claim — automatically."
Status

Where we are.

Stage
Prototype
Investor demo ready
Next Step
Shadow Run
Real data · no patient impact
Goal
Series Seed
Network + data access

This prototype demonstrates the full pipeline — intake to claim — with real AI (Claude Sonnet) and simulated DICOM data. The shadow run phase connects to real hospital data in read-only mode: validating routing logic, license checks, and coding accuracy without touching clinical workflow.

Radiant Network — STAT routing live demo
STAT · FL · Neuroradiology
IMLC routing · 1 of 5 eligible
Claim submitted · $374.66
Pricing

Two tiers. Aligned with outcomes.

Traditional billing companies charge 4–8% of collections and still employ humans. Radiant charges a flat fee for operations plus a small percentage on claims we file — fully automated, higher accuracy, zero headcount.

Tier 1 · Operations Platform
$3
per study · flat rate
Real-time IMLC routing across your credentialed network
STAT prioritisation — subspecialty & modality matching
Licensing intelligence — renewal alerts, coverage risk scoring
Workload balancing across your radiologist group
Tier 2 · Revenue Cycle Automation
1.5%
of net collections · only on claims we file
Claude Sonnet AI pre-analysis surfaced before the radiologist reads
Auto CPT / ICD-10 coding — sign-off to coded claim in seconds
Claim submitted to clearinghouse automatically
Denial rate target <2% vs. industry average of 5–10%
A 10-radiologist group reading 5,500 studies/month pays $16,500 in routing fees plus ~$10,700 in billing automation — replacing 2–3 coders at $9–17k/month, while recovering an estimated ~$21k/month in previously denied or delayed claims. Net ROI positive from day one.
Roadmap

What's coming next.

The shadow run phase validates the core loop. These are the next layers we build once we have real workflow data.

Revenue Cycle Intelligence

Beyond claim submission.

Denial tracking, payer trend analysis, and auto-appeal suggestions — closing the loop between claim submitted and payment received.

QA & Peer Review

Compliance built into the workflow.

Randomized peer review assignment, discrepancy tracking, and performance analytics — satisfying accreditation requirements without extra admin overhead.

Practice Analytics

Visibility for group leaders.

RVU tracking, turnaround time by modality, radiologist workload benchmarking — giving practice managers the data they need to run a tighter operation.

Ready to see this with your data?

We're onboarding shadow run partners now — read-only access to your workflow data, no patient impact, full validation of routing logic, license compliance, and billing accuracy before you commit to anything.

Request a Shadow Run Investor Inquiry View Pitch Deck
Sam Brooker  ·  radiantnetwork@proton.me  ·  radiantnetwork.ai