Primary / Edge

Edge AI Deployments

AI models that run on devices, not the cloud, for low latency and privacy. We compress models and ship them with ONNX, TensorRT, Core ML, and llama.cpp.

Service overview

FocusPrimary / Edge
EngagementFixed-scope or dedicated
TimelineFrom 4 weeks
Ownership100% yours
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How we deliver

Our process for edge ai deployments

A fixed four-step path from first call to production — with weekly demos and a hard launch date.

Days 1–4
Step 01

Discovery & data audit

We map your use case, evaluate data readiness, and define success metrics and guardrails up front.

Deliverable

Feasibility report & eval plan

Days 5–9
Step 02

Model & pipeline design

We architect the retrieval, model, and orchestration layers with cost, latency, and safety in mind.

Deliverable

Architecture & prompt/eval harness

Weeks 2–3
Step 03

Build, evaluate & harden

We build with a regression eval suite, add guardrails against prompt injection and PII leaks, and tune quality.

Deliverable

Tested system & eval dashboard

Week 4
Step 04

Deploy & monitor

We ship to production with telemetry, cost controls, and one-click rollback, then hand over full ownership.

Deliverable

Live system, docs & handover

See where your project fits.

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AI where the data lives

Some tasks can't wait for a cloud round-trip. We deploy AI models directly to devices, factories, and local sites where latency, privacy, or bandwidth are critical.

  • Model compression: We optimize models for specific hardware—GPUs, NPUs, and CPUs—using techniques like quantization and pruning.
  • Runtime engineering: We use ONNX, TensorRT, Core ML, and llama.cpp to ensure models run within strict memory and power limits.
  • Fleet management: We handle remote updates, device health monitoring, and rollback across large groups of devices.
  • Hybrid systems: We build setups that run locally but can use the cloud as a fallback or for data processing.

Performance targets

Edge AI starts with a hardware budget. We design for your specific devices and measure every watt and millisecond.

  • Latency: We aim for single-digit millisecond response times on standard edge hardware.
  • Efficiency: We size models to fit your device's memory while maintaining high accuracy.
  • Reliability: All deployments include full monitoring and verified updates from day one.

Ready to build edge ai deployments?

Every project starts with a clear scope and a fixed timeline. Tell us what you're building and we'll reply within one business day.