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LEAF Labs · AI on the Edge

AI that runs where the data is generated.

We engineer AI models that run directly on devices, gateways and local servers: close to sensors and machines, on hardware with real limits on compute, memory and power.

Discuss an edge AI use case
What It Is

Inference close to the source.

Edge AI moves model inference out of a remote data center and onto the device, or onto a local node next to it. Data is processed where it is produced, and only results, events or summaries need to travel.

Depending on model architecture, workload and target hardware, edge inference can substantially reduce network latency and cloud dependency. It also introduces constraints that cloud AI does not have, so every edge project starts from the hardware, the data and the operating environment, not from the model alone.

Why the Edge

When local processing is the better design

Low-latency local processing

Decisions are taken next to the sensor or machine, without a network round trip. Useful when a response must follow an event quickly and predictably.

Privacy by locality

Raw data such as images, audio or process signals can stay on the device or on site. Only the information that is actually needed leaves it.

Reduced cloud dependency

Less data to transmit and store centrally, and fewer calls to remote services. The cloud remains available for training, fleet management and aggregated analysis.

Resilience and offline operation

Systems keep working when connectivity is intermittent or absent: in the field, in plants, in remote sites and in shielded environments.

What We Build

Edge AI systems, from sensor to decision

Sensor processing pipelines

Acquisition, filtering, feature extraction and inference on vibration, acoustic, environmental, electrical and image data. We design the whole chain, because model quality depends on how the signal is captured and prepared.

Embedded AI on constrained hardware

Models selected and adapted for devices with limited compute, memory and energy, using techniques such as quantization, pruning and knowledge distillation, and runtimes optimized for the target chip.

Edge and cloud architectures

A clear split between what runs locally and what runs centrally: data flows, synchronization, model updates, monitoring and the behavior of each node when the network is unavailable.

Industrial deployment

Integration with existing machines, controllers and plant systems, with attention to the physical environment, maintenance access and the people who will operate the system every day.

How Labs Works

Prototype, engineer, validate, productize.

01

Assess the constraints

Data sources, response-time needs, connectivity, power budget, environment and unit cost define what the device must do.

02

Prototype on real hardware

We build a first version on representative hardware and data, so the trade-offs between model size, accuracy and speed are measured, not assumed.

03

Validate in the field

The system is tested in its operating conditions, where noise, temperature, interference and real usage often differ from the lab.

04

Productize

Update mechanisms, monitoring, documentation and handover turn a working prototype into a system that can be deployed and maintained.

Target Hardware

Chosen for the workload, not the other way round

We work across the range of edge hardware, for example:

  • GPU-accelerated modules such as NVIDIA Jetson, for vision and heavier models
  • Dedicated inference accelerators such as Google Coral, or Raspberry Pi 5 with a Hailo accelerator (used by LEAF for camera and video inference)
  • Industrial PCs and gateways close to machines and lines
  • Microcontrollers, for very small models on low-power sensor nodes

Actual performance depends on the model, the runtime and the workload. We measure it on your target hardware rather than quote generic figures.

Explicit Limits

When the edge is not the right answer

  • Very large models may not fit the memory, power or cost of the target device.
  • Compression techniques can reduce accuracy, and the loss must be measured case by case.
  • A distributed fleet of devices is harder to update and monitor than a central service.
  • When connectivity is reliable and latency is not critical, cloud inference can be simpler.

We say so during the assessment when a centralized or hybrid architecture fits the problem better.

From Our Labs

Argo: edge AI applied to sensing.

Argo is the wireless sensor acquisition system developed in LEAF Labs. It combines synchronized sensing across distributed nodes with local machine learning inference, and it is where much of our edge AI work is tested in real conditions.

Explore Argo About LEAF Labs
FAQ

Questions & Answers

Common questions about AI on the Edge.

Ask Us Anything

AI on the Edge means running AI models directly on devices, gateways or local servers, close to where data is generated, instead of sending every input to a remote cloud service for processing. At LEAF it is a Labs capability: we design, engineer and validate edge AI systems on the hardware they will run on.

Cloud AI centralizes compute and scales easily, but every request travels over a network. Edge inference runs the model locally. Depending on model architecture, workload and target hardware, this can substantially reduce network latency and cloud dependency. The trade-off is limited compute, memory and power on the device, which is why many real systems combine local inference with cloud services for training, fleet management and aggregated analytics.

Typical targets include GPU-accelerated modules such as NVIDIA Jetson, dedicated inference accelerators such as Google Coral or a Raspberry Pi 5 with a Hailo accelerator (which LEAF uses for camera and video inference), industrial PCs and gateways, and microcontrollers for very small models. The right choice depends on the model, the power budget, the operating environment, the expected volumes and the cost per unit.

Common techniques are quantization (using lower-precision numbers for weights and activations), pruning, knowledge distillation into smaller models, choosing architectures designed for efficiency, and compiling the model with a runtime optimized for the target hardware. Each technique trades size or precision against accuracy, so we measure the effect on your data and on the target device before deployment.

It helps. When raw data is processed close to the source, it does not need to leave the device or the site, which reduces exposure. It is not a guarantee on its own: device security, access control, update mechanisms and the data that is still transmitted must be designed deliberately. For private AI infrastructure beyond individual devices, see our Confidential AI capability.

Yes, when the model and its dependencies run locally, inference can continue without network connectivity. The design must also define what happens to results, logs and pending updates while the device is offline, and how it resynchronizes when the connection returns.

Deployed models need a controlled update path: versioning, signed updates, staged rollout and the ability to roll back. Monitoring is needed to detect when real-world data drifts away from the training data. We design this path as part of the system, because the right approach depends on connectivity, fleet size and how accessible the devices are.

Argo is the wireless sensor acquisition system developed in LEAF Labs. It combines synchronized sensing with local machine learning inference on the nodes, and it is a concrete example of how we apply edge AI to sensor data in the field.

Usually with a technical assessment of the use case: data sources, latency and connectivity requirements, hardware constraints and the deployment environment. Labs can then build a prototype on representative hardware and validate it before productization. If the use case is not defined yet, LEAF Advisory can help identify and prioritize it first.

Have data that should be processed where it is generated?

Tell us about your sensors, devices and constraints. We will tell you whether edge AI fits, and what a first prototype would look like.

Talk to LEAF Labs See Confidential AI