System ready: v1.2.4 deployment

Stop training models on redundant spatial data. Deduplicate vision pipelines at scale.

Q: How is Voxelion different from file hashing or checksums?

Checksums only catch byte-identical files. Voxelion fingerprints the spatial content itself, so it still detects near-duplicate frames after re-encoding, metadata rewrites, or minor sensor noise.

Q: Does Voxelion need access to patient data or PHI?

No. Deduplication is metadata-independent and operates on voxel intensity signatures, so DICOM headers can be stripped or anonymised before ingestion.

Q: What throughput can the engine sustain?

The Rust core performs sub-millisecond vector comparison against petabyte-scale signature libraries and runs asynchronously, so it can sit inline with high-frame-rate sensor streams without becoming the bottleneck.

Q: Can I run Voxelion inside my own VPC?

Yes. The Enterprise plan supports on-premise and private-cloud deployment with custom engine tuning and dedicated SRE support.

Q: Which formats are supported?

Voxelion Health handles 3D DICOM and NIfTI volumetric imaging. Voxelion Drive handles LiDAR point clouds and high-rate camera or radar frame streams.

An asynchronous, high-throughput perceptual data integrity engine built for 3D DICOM volumes and high-frame-rate spatial sensor streams. Eliminate data leakage and purge repetitive background noise before it ever reaches storage.

Get free API key Read docs & Python SDK

No card required
5,000 free requests
Metadata-independent

terminal — voxelion-percept-v1

Engine domain

Status: ingesting…

{
  "session_id": "vxl_89234",
  "engine": "Health_3D",
  "frames_processed": 1420,
  "redundancy_detected": 0.42,
  "status": "DEDUPLICATED",
  "integrity_score": 0.998,
  "savings": {
    "storage_cut": "34.2%",
    "annual_usd": "$12,402"
  }
}

Trusted by perception engineers, radiomics labs, and vision infrastructure leaders

DATACORE
NEURAL_X
SPATIAL_V
BIO_SYSM
AUTO_NAV

The petabyte bottleneck

Redundant data is ruining real-world model performance

Spatial sensors sample far faster than the world changes. What lands in your bucket is mostly the same scene, over and over.

Hidden spatial redundancy

Sensors capture thousands of near-identical frames that bloat datasets without adding a single bit of new information.

30–60% typical overlap

False accuracy & overfitting

Duplicates split across train and validation leak answers. Models memorise specific voxels instead of learning generalised features.

Silent validation leakage

Millions burned in storage

Infrastructure cost scales linearly with raw volume, yet a large share of spatial data provides zero marginal information gain.

Cost scales with noise

Purpose-built domains

One platform. Two engines.

Voxelion Health

3D DICOM & volumetric imaging

Slice-by-slice voxel intensity hashing
Multi-modal registration alignment
Anonymised, metadata-independent deduplication

Voxelion Drive

LiDAR & high-rate sensor streams

Point-cloud proximity & overlap analysis
Temporal-spatial drift compensation
Cross-frame optical flow validation

Architecture

Built for high throughput.

Voxelion acts as a pre-storage filter, so only high-entropy, unique spatial data enters your cold storage or training buckets.

Volumetric fingerprinting

Unique 512-bit signatures generated for every 3D sub-volume in the stream.

Sub-millisecond vector comparison

An optimised Rust core compares incoming frames against petabyte-scale libraries instantly.

Metadata-independent

Integrity is verified from actual spatial content, not header files that can be rewritten or corrupted.

main.py — python-sdk

Active

import voxelion

# Initialise the high-throughput engine
engine = voxelion.Client(api_key="VXL_...")

# Define your ingestion pipeline
pipeline = engine.create_pipeline(
    engine_type="spatial_drive_v2",
    threshold=0.98,
    on_redundant="drop",
)

# Process the sensor stream
for frame in sensor_stream:
    result = pipeline.verify(frame)
    if result.is_unique:
        training_db.insert(frame)
    else:
        log.info(f"Redundant frame dropped: {result.id}")

512-bit

Volumetric signature
<1 ms

Vector comparison
34.2%

Median storage reduction
0.998

Integrity score

Infrastructure pricing

Scale data integrity alongside model growth

Start free. Pay for processed volume, not seats.

Developer sandbox

$0/month

5,000 requests / month
Single project access
Community support

Start coding

Questions perception teams ask first

How is this different from file hashing or checksums?

Checksums only catch byte-identical files. Voxelion fingerprints the spatial content itself, so it still detects near-duplicate frames after re-encoding, metadata rewrites, or minor sensor noise.

Does Voxelion need access to patient data or PHI?

No. Deduplication is metadata-independent and operates on voxel intensity signatures, so DICOM headers can be stripped or anonymised before ingestion.

What throughput can the engine sustain?

The Rust core performs sub-millisecond vector comparison against petabyte-scale signature libraries and runs asynchronously, so it can sit inline with high-frame-rate streams without becoming the bottleneck.

Can I run Voxelion inside my own VPC?

Yes. The Enterprise plan supports on-premise and private-cloud deployment, with custom engine tuning and dedicated SRE support.

Which formats are supported?

Voxelion Health handles 3D DICOM and NIfTI volumetric imaging. Voxelion Drive handles LiDAR point clouds and high-rate camera or radar frame streams.

Deploy in minutes

Purge spatial noise from your pipelines in under 60 seconds.

Create free sandbox Request enterprise demo

Metadata-independent by design · No PHI required