The full stack of physical intelligence.
We design the hardware. We build the software. We own both. Every capability below is engineered, integrated, and ready for pilot deployment.
Cities change faster than static maps can track. We break every deployment area into a grid of operational cells and assign prioritized, repeating capture to each — the architecture that turns the physical world into a live, queryable dataset.
- Grid resolution
- 52 cells per LGA (flagship design)
- Capture model
- Crowdsourced driver network
- Categories defined
- 39 across 14 groups
- Update cadence
- Continuous refresh (by design)
Robotics that pass lab tests in temperate climates rarely survive a real Nigerian street. We design rigs and autonomous platforms that account for unreliable power, intermittent bandwidth, dust, heat, and terrain that wasn't in anyone's training set.
- Form factors
- Vehicle-mounted, aerial, portable
- Autonomy level
- Driver-in-the-loop (L3 equivalent)
- Operating envelope
- Designed for field reality
- Integration
- Open pipelines to our AI stack
Our hardware is specified, integrated, and tested around the job it needs to do. 360° capture, GPS precision, swappable storage, status-verified operation — every component is chosen to survive the field, not the showroom.
- Camera systems
- Insta360 X5 integrated rigs
- Storage
- Hot-swap microSD, field-verified
- Positioning
- GNSS with local correction
- Power
- Designed for intermittent supply
Pre-trained models fail on streets they've never seen. Our pipelines are built around YOLO26 with YOLO11 fallback, fine-tuned continuously on local data, and benchmarked against physical captures — not scraped datasets.
- Primary model
- YOLO26 (object detection)
- Fallback
- YOLO11 (stability tier)
- Training data
- Proprietary African corpus
- Benchmarking
- Field-verified ground-truth
Want to see it in the field?
We run live demos on request. Tell us what you need to see and we'll set it up.