Live deployment · Industrial pilot

AI That Learns in simulation, deploys in Reality.

We build vision AI for the events you can't film — perception models trained on synthetic data from CARLA and NVIDIA Omniverse, deployed on real cameras. From factory floors to public roads. No manual labels, no data collection campaigns, no domain gap.

See How It Works → Try the demo → See Solutions →
01 / Pipeline

From Physical Object
to Deployed Intelligence.

Five stages, fully automated. Every step is reproducible, every label is generated, and every model improves the next iteration — without a human ever drawing a bounding box.

01 — STEP
Scan

Real object to 3D model

Photogrammetry on the physical hazard. The asset carries real geometry and real surface response.

02 — STEP
Simulate

Load in photoreal environment

Drop into CARLA or NVIDIA Omniverse. The simulator becomes a sampler over every condition the model will face.

03 — STEP
Generate

Millions of labeled images

All lighting, all angles, all weather. Ground truth — bounding boxes, masks, depth — emitted alongside every frame.

04 — STEP
Train

Detection without annotation

SynYOLO — our YOLOX-based detector — learns directly from synthetic frames. No annotation teams, no labeling queue, no humans in the loop.

05 — STEP
Deploy

Live on real cameras

TensorRT inference on industrial RTSP feeds or in-vehicle dashcams. Pixel detections become real-world coordinates via camera calibration.

02 / Solutions

One engine, multiple worlds.

Three deployments that run on your cameras with nothing to train, and one path for the objects only you have. Live on a factory floor today, and already spotting lost cargo on the motorway.

Try them on the demo →

Factory floor with a taped-off press, the enclosed floor filled as a no-go zone and a worker inside it labelled UNSAFE at 0.03 m ● zone + person distance
Ready to deploy — no training

No-Go Zone Mapping

Reads whatever the crew put down, tape, cones, chains or fences, returns the enclosed floor as a zone, and measures how far every person is from it.

Explore the solution →
Workshop floor with an oil spill grounded as a hazard and the worker beside it masked red and flagged PPE FAIL ● hazard · exposure check
Ready to deploy — no training

Hazard & PPE Auditing

Treats every frame as a safety walk. Lists what is in the scene, judges what could hurt someone, and checks who is standing next to it and what they are wearing.

Explore the solution →
Dashcam frame on the M25: drivable road surface segmented in blue, cargo falling from a truck segmented as debris segmentation · dashcam
Prototype — Validated on real footage

Road Debris Detection

Segments the drivable surface first, then flags anything on it that is not a vehicle. Rockfall, lost cargo, potholes, animals. The class list is a text file.

Explore the solution →
Factory camera feed with a detected cone and world-coordinate grid ● live · cam-04
Built for your objects — Live pilot

Custom Objects, Trained in Simulation

When the thing you need is in no vocabulary, we render it and train a detector you keep. The cone pilot runs live on a factory camera, mapped to floor coordinates.

Explore the solution →
03 / Why this works

A pipeline that replaces the bottleneck.

Real-world data is expensive to collect, dangerous to stage, and impossible to label fast enough. We replace all three problems with a single rendered pixel.

01 / VALUE

Zero Manual Labels

No annotation teams. No data collection campaigns. No labeling queue running ahead of you forever. The simulator emits ground truth — every box, every mask, every depth value — alongside the image it just rendered.

02 / VALUE

Days, Not Months

A new hazard class — from 3D capture to production model on a live camera — in days to weeks, not quarters. New failure modes in the field become a new render pass, not a new data collection campaign. Retrain and redeploy in the same sprint.

03 / VALUE

Sim-to-Real Transfer

Models trained entirely in simulation work in reality. Photoreal rendering, domain randomisation, and PBR materials close the gap — verified on live factory footage the model has never seen.

04 / Backed by

Trusted by the programs that build AI infrastructure.

Active members of the leading startup programs from NVIDIA, AWS, and Microsoft — and a partner in Austria's 5G LUMEIK testbed.

Member
NVIDIA
Inception
NVIDIA Inception
Omniverse · GPU credits
Member
AWS
AWS Activate
Startups
Cloud · inference
Member
MS / for
Startups
Microsoft
for Startups
Azure · GTM
Partner
5G
LUMEIK
5G LUMEIK
Austrian 5G testbed