Smart Infrastructure & Physical Environments
Fixed installations are Physical AI without robots. Cameras and LiDAR on a corridor, a terminal, a venue or a campus perceive, predict and trigger action — under privacy rules that make cloud video analytics a non-starter.
- Fixed rigs
- Perception where nothing moves but the environment is understood
- On-prem
- Video and spatial data stay inside the operator's boundary
- Real-time
- Analytics that drive an operational response, not a dashboard
Why this sector cannot use hosted AI
The constraints below decide the architecture. Everything else follows from them.
Privacy and public trust
Public-facing video carries obligations that hosted analytics cannot satisfy. Processing happens locally, with retention and redaction policies enforced at the pipeline level.
Sensor sprawl
Sites accumulate incompatible cameras, LiDAR, access control and sensors over decades. The engineering is in calibration, synchronization and a single spatial frame — not in the model.
Operations, not dashboards
Value appears when perception triggers a workflow: dispatch, gate control, crowd routing, maintenance. Analytics that stop at a screen do not change outcomes.
What Enfuse builds here
Delivered by embedded engineers working inside your environment, not handed over as a slide deck.
Multi-sensor spatial perception
Camera, LiDAR, thermal and radar fused into one spatial frame across a site, with objects tracked in site coordinates.
- Site-wide calibration and time synchronization
- Cross-sensor tracking and re-identification
- Occupancy, flow and dwell-time analytics
Real-time video analytics
DeepStream pipelines running at the edge for detection, classification and event triggering across large camera counts.
- Edge inference sized per camera group
- Event routing into existing operations consoles
- Retention and redaction policy enforced in-pipeline
Digital twins of physical sites
Gaussian-splat and simulation-based twins that give operators a spatial view of live activity instead of a wall of video tiles.
- 3D reconstruction and SLAM-based mapping
- Live track projection into the site twin
- Scenario simulation for events and incidents
Hardware and software we deploy
Specified per site against sensor load, latency budget, thermal envelope and security boundary.
- NVIDIA Jetson Orin NX / AGX Orin at the edge
- Central B200 / H200-class server for twins and re-identification
- DeepStream, TensorRT, Isaac Sim / Omniverse
- Ouster / Hesai LiDAR, GMSL2 camera arrays, thermal imagers
Bring us your constraints
Sensors, security boundary, latency budget, existing systems. We will tell you what is realistic and what it takes.