Privacy-first

On-Device & Data-Sovereign AI Consulting

Move inference off the cloud — design on-device agentic systems that preserve data sovereignty, reduce latency, and achieve inherent compliance. No third-party data exposure.

Service Overview

For regulated industries, defense-adjacent work, or any team where "we send your data to OpenAI" is a non-starter, this is the alternative: AI systems that run inference on-device, with no data leaving your infrastructure by design.

What's Included

  • on-device architecture design
  • model selection for edge constraints
  • data-sovereignty audit
  • and deployment support for the target hardware environment.

Tools & Technologies

Ollama (local runs)Llama.cppONNX RuntimeGGUF / AWQ Quantized ModelsWebGPU / WASM AI BindingsPythonRust

Frequently Asked Questions

Does on-device AI mean smaller, worse models?

Not necessarily — the tradeoff is real but manageable with the right model selection and quantization strategy for your hardware.

Is this only for mobile/IoT, or does it apply to on-prem enterprise deployment too?

Both — the core principle (inference where the data already lives) applies to on-prem servers as much as edge devices.

How does this affect compliance posture?

Data that never leaves your infrastructure sidesteps a large category of third-party data-processing agreements and associated audit burden.

Is this for you?

  • Sending data to a third-party API is a compliance or security blocker, not a preference
  • You need an AI security consultant who understands the difference between "encrypted in transit" and "never transmitted"
  • Latency matters and cloud round-trips aren't acceptable