Diagnostic Infrastructure / v1.0

Radiology, rebuilt with software.

The operating system for diagnostic imaging. Workflow infrastructure, AI acceleration, and real-world clinical deployment.

Scans annually
0.0B+
Of clinical decisions rely on imaging
0%
Radiologist capacity
Global shortage
Time-to-decision
Hours → Minutes
01 — Context

Modern medicine runs through radiology.

Stage 01
Scan
Stage 02
Queue
Stage 03
Radiologist
Bottleneck
Stage 04
Report
Stage 05
Treatment

Imaging volumes are rising faster than specialist capacity. The throughput constraint of modern healthcare lives at the radiologist's worklist.

02 — Platform

Workflow. Intelligence. Feedback.

  • 01
    Imaging enters the system
    DICOM ingest from any modality, any vendor.
  • 02
    AI triage analyzes
    Proprietary models score urgency in seconds.
  • 03
    Findings prioritized
    Critical cases routed to the top of the queue.
  • 04
    Radiologist reviews
    AI second-read embedded in the worklist.
  • 05
    Final report generated
    Structured output integrated with RIS/PACS.
  • 06
    Feedback loop
    Production signals retrain the next model.
03 — Approach

We don't replace radiologists. We give them capacity.

01

Workflow layer

Built around radiologists and teleradiology partners — not around them.

02

AI layer

Proprietary models with a compression-driven training and inference edge.

03

Data layer

Public, synthetic, and partner-sourced data — radiologists in the loop.

04

Deployment layer

Integrates with existing clinics and teleradiology infrastructure.

04 — Core IP

.vero — our compression engine.

.vero is our in-house medical imaging compression engine — a foundational piece of infrastructure that changes the economics of training, storing, and moving 3D imaging data. It is a durable, defensible advantage that compounds across every layer of the platform.

0×
Compute efficiency

Training runs that took two weeks compress to hours on the same hardware.

0×
Smaller files

A 1.1 GB study reduced to roughly 100 MB — without losing what models need to learn.

0×
Fewer spatial positions

Enables larger models, more data, and more experiments per budget.

Training

Iterate faster and train larger models on the same GPU footprint.

Cost

Dramatically lower compute and storage cost per study processed.

Transfer

Move large 3D volumes across clinics and networks in a fraction of the time.

Technical details of the .vero engine are proprietary and shared with partners under NDA.

05 — Initial focus

Starting with the highest-value modalities.

Modality 01

CT

Urgent and high-value. Stroke, bleed, trauma, oncology staging — minutes matter, and volumes are accelerating worldwide.

Minutes
Time-to-flag for critical findings
Modality 02

MRI

Highest complexity. Long backlogs in neuro and musculoskeletal — large 3D volumes where our infrastructure advantage is greatest.

3D
Large-volume studies, accelerated
Adjacent modalitiesChest X-RayMammographyUltrasoundPET
06 — Impact

From delay to decision.

For radiologists
0×
Faster reporting
  • Faster reporting
  • Lower cognitive load
  • AI second reader
For healthcare systems
0%
Throughput uplift
  • Faster throughput
  • Better patient outcomes
  • Better resource allocation
For patients
0%
Reduction in wait time
  • Shorter waiting time
  • Faster diagnosis
  • Faster treatment
07 — Infrastructure moat

Built for scale. Designed for regulation.

Phase 01
Development
Phase 02
Validation
Phase 03
Certification
Phase 04
Deployment
Human-in-the-loop
Audit trails
Compression edge
Clinical validation
Multi-market readiness
08 — Vision

From scan to treatment in minutes.

The infrastructure layer for the next generation of diagnostic medicine.