On-premises AI, local LLM deployment, edge devices, and GPU infrastructure for businesses that need tighter data control.
On-premises AI, local LLM deployment, edge devices, and GPU infrastructure for businesses that need tighter data control.
✓Exceptions are the real workload Simple automation stops when inputs vary, context is missing, or an approval decision matters.
✓Nobody can explain the handoff Teams lose confidence when an AI output appears without evidence, ownership, or a correction path.
✓01 Define Choose one use case, baseline, approval boundary, and result the team can observe.
✓02 Connect Give the system only the tools and context it needs, with least-privilege access.
✓03 Evaluate Test normal work, edge cases, failures, and handoffs before expanding the scope.
Reduce the cost of repetitive work and expensive rework, so your team can handle more customers and spend more time on work that produces revenue.
Client-owned assets · Human approval gates · Inspectable handoff · No outcome guarantees
Keep selected workloads closer to the systems and people responsible for them, with access and retention documented.
Evaluate on-site inference when network dependency or response time is part of the real requirement.
Compare hardware ownership, model serving, updates, monitoring, and support against the cloud alternative.
Evaluate open and hosted models against quality, maintenance, licensing, and operating constraints.
Run capable open models inside your own environment, tuned to your hardware.
✓Open-weight model serving
✓Quantization & optimization
✓Private RAG over your data
✓GPU server & cluster setup
Inference at the network edge for real-time, offline-capable workloads.
✓NVIDIA Jetson & Coral TPU
✓Vision & sensor inference
We don't hand you a box — we hand you a working, monitored system.
✓Hardware selection & procurement
✓Updates & security patches
Show us the process, systems, exceptions, and approval points. We’ll help identify a practical first scope and the evidence needed to measure it.
✓Which inputs, systems, and decisions repeat often enough to model?
✓Where must a person approve, correct, or stop the workflow?
✓How will the team test reliability before expanding the scope?
Share the workflow, customer path, or business constraint you want to improve. We’ll review it and respond with the most useful next step.
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Pick a time for a focused working conversation about the process, systems, constraints, and next decision. This is a request—not a promise of a particular result.
Local deployment can help when data residency, network dependency, latency, or predictable infrastructure ownership matters. It also adds hardware, update, monitoring, and support responsibilities that should be scoped honestly.
From edge devices like NVIDIA Jetson and Coral TPUs to on-prem GPU servers and small clusters — sized to your models and throughput, not oversold.
Yes. We deploy and optimize open-weight LLMs (Llama, Mistral, Qwen and others) locally with quantization and serving tuned for your hardware.
We can scope hardware selection, deployment, model serving, monitoring, updates, and support as separate responsibilities. You receive a working handoff and an operating runbook—not just a parts list.
We map the highest-cost workflow first — the calls, forms, inboxes, CRMs, and handoffs involved — before anything is built.
A scoped build with approval gates and human-in-the-loop exception paths, around the tools you already use.
The workflow runs live with logs your team can inspect, so value is proven — not promised.
Docs, logs, and the code itself are yours. No lock-in, no renting your own system back.
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Start with a free AI audit. We map the workflow slowing you down and show you the smallest useful fix — before you pay for a build.
Get my free AI audit Call (425) 780-5858