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Edge ML and perception AI for smart cities.

DehazeLabs builds production computer vision and perception data platforms for smart-city systems — automated enforcement, traffic analysis, and urban operations. Reference deployment: Hayden AI, operating across US cities at municipal scale.

→ What we build for smart cities

Smart-city AI is a physical-world problem. Cameras on vehicles and at intersections. Edge compute with intermittent connectivity. Inference that needs to run in real time, in the field, without cloud round-trips for every frame. The data platform behind it — perception logs, annotation pipelines, model versioning, evidence packaging — is as important as the model itself.

Our reference engagement is Hayden AI, where we built the edge ML inference pipeline and perception data platform powering automated traffic enforcement across US cities. The platform handles classification at vehicle-mount scale, packages enforcement evidence, integrates with municipal agency review workflows, and manages model updates across a distributed fleet.

Smart-city AI systems we build

Perception data platforms

Ingestion, annotation, versioning, and model evaluation pipelines for camera-based perception systems. Designed for the volume and variety of real-world smart-city feeds — heterogeneous camera hardware, variable lighting, mixed urban environments.

Edge inference pipelines

Object detection, classification, and tracking optimized for on-device inference. Model compression and quantization for the specific edge hardware in your deployment. Tiered pipelines when full edge inference isn't feasible — pre-filter at the edge, confirm in cloud. See our full breakdown of taking CV systems to production.

Enforcement and evidence backends

Production-grade evidence packaging, chain-of-custody logging, and agency-facing review interfaces for automated enforcement programs. Built to satisfy municipal legal and audit requirements.

Traffic analytics and operations

Aggregated traffic flow analytics, incident detection, and operational dashboards from perception data. Real-time stream processing for operational use cases, batch analytics for planning and compliance reporting.

[ What production looks like ]

From field hardware to municipal operations.

Edge inference
Real-time
Classification running on-device at capture, no cloud round-trip for primary enforcement decisions
Evidence packaging
Automated
Violation evidence assembled, logged, and routed to agency review without manual processing
Fleet coverage
Multi-city
Model updates and platform operations across distributed fleets in multiple municipalities

→ Related reading

Computer vision production deployment → AI for logistics → Edge & Perception AI →
[ FAQ ]

Smart-city AI — common questions.

What AI systems does DehazeLabs build for smart cities?
DehazeLabs builds: perception data platforms (computer vision inference pipelines fed by vehicle-mounted or fixed cameras), edge ML inference systems running classification and detection models at the point of capture, automated enforcement backends (violation detection, evidence packaging, agency review workflows), and traffic analytics platforms. Reference deployment: Hayden AI — smart-city edge ML and perception data platform for automated traffic enforcement across US cities.
How do you handle edge inference constraints in smart-city deployments?
Edge inference for smart cities runs under real constraints: intermittent connectivity, power budgets, heterogeneous hardware, and strict latency requirements for enforcement use cases. We design inference pipelines for the hardware that exists in the field — not just cloud inference with edge preprocessing. Models are optimized for the specific edge device class. Where full offline inference isn't feasible, we build tiered pipelines with edge pre-filtering and cloud confirmation.
What's the typical engagement for a smart-city perception project?
Smart-city perception engagements typically run 6–18 months, $400K–$2M. The first phase covers model development, edge inference pipeline, and data platform architecture. The second phase covers production deployment across the initial fleet or fixed installation and monitoring infrastructure. Ongoing operations are common — drift detection, model retraining pipelines, and platform evolution as the deployment scales to new cities or use cases.

Building perception AI for municipal or urban systems?

Tell us about your edge hardware, the use case, and what production looks like for you. We'll tell you what it would take to get there.

Schedule a conversation → Edge & Perception AI