Edge AI Development Services & Custom Solutions

We build edge AI solutions around one target device: the model, the inference pipeline, and the firmware integration. The work starts from your power and latency budget rather than from a model zoo, because on constrained hardware the budget eliminates most of the options before accuracy is even discussed.

Vision is where edge constraints bite hardest, since every frame has to be captured, preprocessed, and inferred inside a fixed time slot. We build detection, segmentation, tracking, and pose estimation that run on the device itself, from machine vision on embedded systems in a factory to a cortisol test reader that works on an ordinary phone camera.

We assess whether your idea survives contact with the hardware you have in mind, using your data and your target board. If the honest answer is that your accuracy target does not fit in your power budget, you hear it before system design starts.

Training a deep learning model that hits your accuracy target is the first half. Model compression takes it the rest of the way: quantization, pruning, and knowledge distillation until it meets the latency and memory limits of the board. Then we verify that the accuracy survived the process on your own validation data.

A model that runs in a notebook is not yet a product. We handle the export path, the runtime integration, the hardware delegate configuration, and the on-device benchmarking and performance optimization that prove the numbers hold on the production board.

Fleets drift. Cameras get repositioned, lighting changes with the season, and the data your model sees in month six is not what it was trained on. Edge MLOps covers over-the-air updates with signed packages and device authentication, versioned rollouts, security patches, and monitoring that tells you when field accuracy has started to slide.

Streaming every camera to a server gets expensive fast, and most of those frames contain nothing worth storing. We deploy video and data analytics directly on cameras and gateways, so only events and metadata travel to the cloud.

Audio models fit the edge well: they are small, they run continuously, and their raw data is often the most privacy-sensitive thing a device captures. We build keyword spotting, acoustic event detection, and audio classification that run on microcontrollers and IoT devices without a network connection.

Half of an embedded vision system's accuracy is decided by the imaging setup, before any model runs. Our physics background lets us specify sensors, optics, and ISP parameters alongside the model. On constrained hardware that often buys more than a larger network would, because cleaner sensor data costs nothing at inference time.

Moving AI workloads from the cloud onto a device is one of the most frequent requests we get for edge AI services, and it is rarely a straight port. We re-architect models whose operators have no hardware support, replace layers that collapse under low-precision inference, and rebuild the preprocessing that quietly ran on a server before.