Syntheta AI
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Data multiplication · physical AI

Capture once.
Train every condition.

Syntheta helps physical-AI teams get more useful training experience from every real capture. Turn factory, robot, drone, and sensor footage into trusted data for the situations that are hardest to collect.

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Original capture · clip-01-v2 · 10s 1072 × 480
Why this matters

Capture is the bottleneck.

5–30 min
of real capture to start a dataset run
Multi-class
industrial taxonomy with masks and boxes
Validated
variants with provenance and quality gates

Real-world capture is slow and rarely covers every operating condition. Syntheta takes the data you already have and creates useful, labeled variations for low light, motion, occlusion, new viewpoints, and difficult edge cases. The result stays tied to the original scene, so your team can inspect it, trust it, and put it to work.

Pipeline · seven stages

One capture.
A labeled dataset.

A source capture flows through restoration, target labeling, controlled generation, validation, dataset export, and model training. Each accepted sample keeps its source, recipe, seed, labels, and validation record.

FRAME01

Label once

Start with the objects and situations your model needs to understand, from pallets and parts to people and safety zones.

detection
FRAME02

NeuroDepth-T4 restoration

Recover detail from difficult footage so your training set is useful in dim, fast-moving, and changing environments.

restoration
FRAME03

Branch A — R2R mutation

Create controlled changes such as lighting shifts, blur, weather, noise, and occlusion while keeping the scene and labels consistent.

synthetic
FRAME04

Branch C — passthrough

Keep a clean version of the original data so every experiment has a clear real-world reference.

baseline
FRAME05

Geometry-conditioned generation

Generate fresh appearances and camera views that still look like the environment your model will actually enter.

synthesis
FRAME06

Branch B — fresh detection + R2R

Every new frame is checked before it reaches training. Weak or inconsistent results are separated out instead of quietly lowering model quality.

synthetic
FRAME07

Summary

Every branch's output manifest rolls up into one summary file for the whole run.

manifest
BRANCHD

Spatial reconstruction + world state

Turn multi-view captures into a spatial view of the scene, then use it to create new training views and action-aware sequences.

3D
How it works

One capture. A larger training set.

01
Bring a real captureFactory, robot, drone, or synchronized sensor data.
02
Define the conditionsChoose lighting, motion, occlusion, views, and sensor variation.
03
Generate and validateProduce new samples while carrying labels and provenance forward.
04
Train on what mattersExport a ready dataset and measure utility on held-out real data.
Contact sheet

From one real capture

Inspect the transformation chain: real capture, restoration, mutation, generated labels, and 3D reconstruction.

Last validated run

Numbers, not adjectives

500
automated tests passing in the current build
1 → many
real capture expanded into controlled training experience
RGB + depth
appearance and spatial outputs from aligned captures
Multi-step
world-model rollouts with motion and sensor state
YOLO / COCO
train-ready detection and segmentation exports
Real → real+
held-out evaluation for training-data utility
Fail closed
invalid labels and geometry stay out of exports
1
provenance contract across every modality
Built on

What's actually running

YOLOv8n-pose
NeuroDepth-T4 · EventCore V8.1
Geometry-conditioned generation
R2R mutation engine
YOLO / COCO dataset export
New viewpoints from real scenes
Future-state prediction for physical AI

Get early access.

Bring a real capture and your target classes. We return an expanded dataset, labels, provenance, and a training baseline.

Sign up for beta