Volume that outgrows a team
Thousands of sensors produce continuous streams. Manual labelling stops scaling long before the deployment does.
Designed for cities that run on AI systems
AI Data Solutions for Smarter Cities
Raw urban data is only valuable if it is actionable. We turn sensor, camera and geospatial feeds into production-grade training data for safety, sustainability and efficiency models.
Building Intelligent Urban Ecosystems
A city generates more data in a day than most enterprises see in a year — and almost none of it arrives ready to train on. We take the raw feeds from cameras, IoT sensors and aerial surveys and turn them into labelled datasets your models can actually learn from, with the consistency that public-facing systems demand.
Where Smart Cities AI Programmes Struggle
Public safety raises the bar
A model that misses an event in a civic system has consequences a retail recommendation never does. Accuracy is not negotiable.
Many systems, one picture
Traffic, waste, water and lighting are separate estates with separate formats that have to be reconciled before anything can be fused.
Operations never pause
Cities run continuously, so the data pipeline behind them has to as well — including through re-training and schema changes.
AI Use Cases We Support
Infrastructure monitoring
Detecting road damage, structural wear and street-furniture faults from vehicle and drone imagery.
Public safety analytics
Crowd density, incident detection and anomalous behaviour across public camera estates.
Environmental intelligence
Air quality, noise and flood-risk models trained on fused sensor and satellite data.
Waste optimisation
Fill-level detection and collection-route planning from bin sensors and vehicle cameras.
Urban planning insight
Land-use classification and change detection from aerial and satellite imagery.
Citizen service automation
Classifying and routing complaints, permits and service requests from text and documents.
Data Types We Handle
- Urban video Multi-camera footage with consistent object, event and zone labelling.
- IoT and sensor streams Time-series from environmental, traffic and utility sensors, annotated for events and anomalies.
- Geospatial imagery Satellite and aerial capture with segmentation masks and change annotation.
- Civic text and documents Complaints, permits and reports classified and entity-tagged for workflow routing.
How We Run the Programme
- Start with the decision We agree what the model must decide before we agree what to label. A taxonomy written backwards from the decision survives contact with real data.
- Pilot, then scale A small annotated batch and a measured agreement score first. Volume only after the guidelines hold.
- Edge cases on purpose Night, rain, occlusion and unusual events are sampled deliberately rather than left to chance.
- Continuous re-labelling As the city changes, the dataset changes. Programmes are run as a service, not a one-off delivery.
Build Smart Cities AI on Data You Can Trust
Tell us what you are building and we will come back with a plan, a sample and a price.