DataSynOps — generate exactly the data you need
A closed-loop engine that synthesises rare conditions with physics-based simulation and generative AI, and attaches the ground-truth labels automatically.
Five steps that close into a loop
Diagnose the gap
We identify the conditions the current model fails on, automatically.
Synthesise the data
Physics-based simulation combined with generative realism refinement.
Auto-label
SAM2 produces pixel-precise polygons, sorted into three confidence tiers.
Retrain and verify
Synthetic pre-training, field fine-tuning, then measured verification.
Deploy and report
Detections and the evidence behind them are exported as a report.
Field results from the last step become the input to the first. As client sites accumulate, so do the data assets and the model performance built on them.
The closed-loop structure is secured as granted patent KR 10-2687011 (method and apparatus for generating training data for AI video analytics models) and published application KR 10-2026-0095246 (recursive AI training-data generation based on image synthesis).
Four components
10 domain generation pipelines
| Pipeline | What it generates | Domain | Status |
|---|---|---|---|
| SewerDefectSynOps | Sewer pipe defect data generation | Infrastructure inspection | Delivered |
| ScenarioDataSynOps | Scenario-based synthetic data generation | Defense & industry | In funded project |
| AerialDataSynOps | Aerial-view object detection data generation | Defense · UAV | Delivered |
| FireDataSynForge | Wildfire detection data generation | Environment | Delivered |
| AlgaeDataSynForge | Algal bloom detection data generation | Water quality | Delivered |
| EventVideoSynOps | Event-based video synthesis for hazard recognition | Worker safety | In development |
| AerialTargetTrackOps | Drone target tracking and aiming support | Defense | Adjacent |
| SensorRestoreOps | Restoration of contaminated autonomous-driving sensor imagery | Mobility | In development |
| WeldInspectSynOps | Inference across weld geometry, process signals and internal quality | Manufacturing QA | Adjacent |
| SewerAutoLabelForge | Auto-labeling on AI Hub sewer dataset | Labeling automation | In validation |
3 granted patents · 2 certified test reports · 2 software registrations
| Type | Title | Number | Date | Status |
|---|---|---|---|---|
| Granted patent | Method and apparatus for generating training data for AI video analytics models | 10-2687011 | 2024-07-17 | Granted |
| Granted patent | Integrated image enhancement and training-data generation using a generative recurrent network | 10-2654017 | 2024-03-29 | Granted |
| Granted patent | Image matting method and apparatus | 10-2624296 | 2024-01-09 | Granted |
| Published application | Recursive AI training-data generation based on image synthesis | 10-2026-0095246 | 2024-12-16 | Filed / published |
| Certified test report | Certified test report — synthetic data solution | CT23-102887K | 2023-12-08 | Issued |
| Certified test report | Certified test report — DataSynOps-alpha | CT24-104451K | 2024-12-16 | Issued |
| Software registration | Software registration — DataSynOps-alpha | C-2024-054600 | 2024-12-23 | Granted |
| Software registration | Software registration — DataSynOps-epsilon | C-2024-054601 | 2024-12-23 | Granted |
Tell us what has to be detected on your site
Tell us what has to be detected on your site. We will first assess whether that data can be generated. If you have sample footage, we will prepare an auto-labeling demonstration with it.