CC Informatics provides remote inspection services for large infrastructure using drones, ground crawlers, 360‑degree cameras, and underwater ROVs to capture high‑resolution imagery. Its patent‑pending AssetScan platform applies AI‑driven machine‑vision to automatically detect, classify and score structural defects, then maps the results onto photogrammetrically generated 3D digital twins that can be exported to CAD/BIM workflows. This enables asset owners to assess and monitor deterioration safely, quickly, and without the need for hazardous on‑site inspections.
Funding
Funding not disclosed
Founders
Product
Problem
Assessing the condition of large infrastructure—such as bridges, buildings, tunnels, and offshore assets—requires personnel to work at height, in confined spaces, or underwater, which is costly, time‑consuming, and hazardous. Traditional inspection methods also produce data that is difficult to integrate into 3D models, limiting the ability to locate and track deterioration accurately.
Solution
CC Informatics offers a suite of remote survey services using drones, ground‑based crawlers, roving 360 cameras, and ROVs to capture high‑resolution photographic data of hard‑to‑reach assets. Their patent‑pending AssetScan platform applies AI‑driven machine‑vision algorithms to this imagery, automatically detecting and scoring structural defects across materials such as concrete, metal, masonry, timber, and roofing. Detected issues are draped onto photogrammetrically generated 3D digital twins, producing exportable meshes, point clouds, and heatmaps that can be imported into CAD or BIM workflows. This workflow enables engineers to quickly visualise, prioritise, and monitor asset deterioration without exposing staff to dangerous environments.
Target Audience
Primary customers are asset owners and managers in civil infrastructure, utilities, and offshore sectors—such as bridge authorities, building maintenance firms, tunnel operators, and port or offshore facility operators—who need safe, efficient condition monitoring of large structures.
Features
- Automated image capture via UAVs, ground crawlers, 360 roving cameras, and underwater ROVs for comprehensive asset coverage
- Machine‑learning models that locate and classify defects (e.g., cracks, corrosion, mortar loss) and assign condition scores
- Generation of georeferenced 3D digital twins using photogrammetry, exportable as meshes, point clouds, or CAD/BIM compatible files
- Heatmap visualisations of identified deterioration overlaid on the 3D model for rapid assessment
- Ability to retrain AI models for new defect types or materials using custom training datasets
- Integration of survey data into standard industry software without requiring specialized hardware on the client side