We optimize and deploy vision models on edge hardware such as NVIDIA Jetson, so analytics run locally with low latency, bounded bandwidth and footage that never has to leave the site.
Edge AI means running inference on devices located where video is captured: a compute module in a cabinet beside the cameras, an industrial PC on a production line, a ruggedized box at a roadside. The alternative, streaming raw video to central servers, costs bandwidth, adds latency and moves sensitive footage off site.
Edge deployment introduces its own engineering problems. Compute and memory are constrained, thermal envelopes are tight, connectivity is intermittent and devices must run unattended for months. A model that works on a workstation GPU often does not fit, or runs too slowly to keep up with the cameras.
Our edge AI service covers the full path from trained model to a fleet of devices in the field: optimization for the target accelerator, multi-stream pipelines that share compute efficiently, containerized deployment and remote management.
The building blocks we combine for each engagement. Scope is defined by the problem, not by a feature list.
Export through ONNX, INT8 and FP16 quantization with calibration, layer fusion and pruning, with accuracy measured before and after so speed never silently costs correctness.
Hardware video decode, batched inference and per-camera scheduling that let a single device process many streams within its thermal and power budget.
Integration with industrial I/O, PLCs, relays, serial devices and local networks so the edge device participates directly in the operation it observes.
Rules, alerting and evidence capture run on the device, with store-and-forward buffering so events survive connectivity loss.
Containerized services, over-the-air model and configuration updates, health telemetry and remote diagnostics for distributed deployments.
Objective benchmarking of candidate platforms against your camera count, model requirements and environmental constraints before any purchase.
Edge engineering is a sequence of constraints. We resolve them in order so each decision is made with the right information.
Camera count, resolution, required frame rate and detection latency are translated into an inference budget per stream, which drives hardware selection and model size.
The trained model is exported to ONNX, converted to the accelerator's native runtime, quantized with a representative calibration set and benchmarked for throughput and accuracy on the actual device.
Decode, pre-processing, inference, tracking and rule evaluation are composed into a pipeline that keeps the accelerator busy and the CPU free, with explicit handling of dropped frames and stream failures.
Services are containerized with Docker, configured for automatic restart and watchdog recovery, and tested through power-cycle, network-loss and thermal scenarios.
Devices register with a management service for telemetry, update distribution and remote access, keeping each deployment maintainable long after installation.
Practical applications we design for, each anchored to an operational decision.
Construction sites, substations, agricultural facilities and roadside installations where uplink is limited or costly.
Healthcare, education and workplaces where video must be analyzed without being transmitted or stored centrally.
Production lines and machinery where a detection must trigger a physical response within a fraction of a second.
Retail chains, logistics networks and city deployments where per-site processing keeps central infrastructure lean.
Low, predictable latency independent of network conditions.
Bandwidth reduced to events and metadata rather than raw video.
Footage stays on site, simplifying privacy and data-residency obligations.
Continued operation through connectivity outages.
Hardware chosen and sized by measurement, not vendor claims.
Fleet-wide updates and monitoring from a single management plane.
Our five-stage process, applied to this service.
Camera inventory, network topology, power, enclosure and environmental conditions are documented along with latency and privacy requirements.
Candidate platforms are benchmarked, the per-device camera allocation is set and the edge-to-cloud communication model is defined.
Models are quantized and validated on target hardware while the multi-stream pipeline and local event logic are built and stress-tested.
Devices are imaged, installed, connected to cameras and downstream systems and verified in place through a commissioning checklist.
Health telemetry, performance trends and model updates are managed centrally as the deployment grows.
Person detection, multi-object tracking and zone intelligence that turn passive camera estates into active monitoring systems.
PPE compliance, restricted access and unsafe-behavior detection that help safety teams intervene before harm occurs.
Vehicle detection, classification, tracking and identifier recognition for roads, sites, fleets and transport hubs.
Tell us about your cameras, your site and your constraints. We will recommend hardware and show you what a single device can realistically handle.