Edge AI Deployment

We've deployed to industrial gateways, NVIDIA Jetson devices, point-of-sale terminals, and standard IoT microcontrollers with constrained memory, and the model optimization approach differs significantly across that range. We assess your specific hardware constraints during scoping rather than assuming a one-size model fits every device class.

Quantization and pruning typically cost a small, measurable accuracy drop, usually in the low single digits, and we benchmark this against your specific use case rather than citing a generic number. If the accuracy tradeoff is too costly for your application, we'll say so and discuss hybrid edge-cloud architectures instead.

We design edge deployments to keep functioning on cached models and local inference during outages, with sync and update queuing resuming automatically once connectivity returns. This is a core part of the architecture, not an afterthought, since intermittent connectivity is the normal condition for most edge fleets, not the exception.

Yes — we build versioned over-the-air update pipelines so you can push model improvements to hundreds of devices without physical access, with staged rollouts so a bad update doesn't hit your entire fleet at once. Rollback to the previous known-good model is built into the same pipeline.