Solution

Edge AI

Inference where the data is created — plant floor, clinical device, vehicle, field operation. Connected or not.

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The problem

Sometimes the datacenter is in the wrong place

Edge deployment is driven by physics and economics rather than by policy. A decision that must happen in milliseconds cannot make a network round trip. Bandwidth for continuous video upload is often more expensive than local compute. And connectivity fails.

The hard part is not one deployment — it is a hundred. Running a model on a single device is a demo. Managing a fleet across sites, with staged updates, version skew, drift monitoring, and devices you cannot physically reach, is an engineering discipline.

Model size is a genuine constraint at the edge, and it forces real capability trade-offs. Pretending otherwise is how edge projects fail.

Scope

What we deliver

  • Model optimization — Quantization, distillation, and compilation to fit the hardware you actually have — frequently industrial PCs rather than datacenter GPUs.
  • Edge runtime deployment — Serving stack sized for constrained devices, with graceful degradation when resources are contended.
  • Fleet management — Staged over-the-air updates with rollback, version tracking, and health monitoring across every node.
  • Offline-first design — Local inference continues without connectivity; results queue and reconcile when the link returns.
  • Central coordination — Model management, aggregated evaluation, and drift detection from one place, without requiring constant connection.
  • Integration to enterprise systems — One-directional, explicitly designed paths back to ERP, MES, or your system of record.
Detail

What edge costs you

Smaller models mean real trade-offs

You will not run a frontier-class model on an edge device. Task selection matters more here than anywhere else — pick problems that small models genuinely solve.

Debugging is materially harder

A failure on a device in a plant you cannot reach is a different problem from a failure on a server. Observability has to be designed in, not added later.

Fleet drift is real

Without disciplined version management you will end up with devices running different models producing different answers. That is a correctness problem, not just an ops one.

Hardware refresh is a capital cycle

Edge nodes are purchased, deployed, and hard to change. Sizing decisions carry longer than cloud ones.

Questions

Frequently asked

Can edge devices run useful models at all?

Yes, for well-chosen tasks. Visual inspection, classification, anomaly detection, and constrained retrieval all run effectively on modest hardware once optimized. Open-ended reasoning does not.

What happens when a device loses connectivity?

Nothing, if it is designed correctly. Inference is local; only management and reporting need the link. If losing connectivity stops production, the architecture is wrong.

How do you update models across a fleet?

Staged rollout with canary nodes, health checks, and automatic rollback — the same discipline as any fleet deployment, applied to model artifacts as well as code.

Related

On-Premise AI

Datacenter deployment, including air-gapped enclaves.

On-Premise AI

LLMOps

Evaluation, drift detection, and lifecycle management in production.

LLMOps

Start with an assessment, not a proposal.

A structured evaluation of your data, infrastructure, security constraints, and candidate use cases — delivered as a prioritized roadmap you own.

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Direct response from an engineer. Typically within one business day.