The information needed to act lives across tools, documents, messages, and experienced team members.
On-Prem & Edge AI
Keep Your AI Fast, Private, and Under Your Control
Run sensitive workloads on local LLM and edge infrastructure, so you protect your data, reduce cloud dependence, and get dependable performance.
The Real Cost
Cloud AI Has Limits—And They're Costing You
Latency, cost, privacy, and control issues make cloud-only AI a poor fit for many workloads. Edge and on-prem solutions give you the performance, security, and economics you need for production AI.
Latency Kills User Experience
Cloud AI API calls add hundreds of milliseconds to every interaction. For real-time applications, edge deployment delivers the responsiveness users expect.
Per-Token Costs Add Up Fast
At production scale, cloud AI pricing can become unpredictable and expensive. On-prem inference reduces costs and gives you predictable expenses.
Data Leaves Your Control
Sending your data to cloud providers exposes you to privacy risks and compliance violations. Edge AI keeps your data where it belongs—under your control.
How We Help
Production AI That Runs Where You Need It
We design, build, and deploy AI systems on edge hardware, on-prem servers, or optimized cloud infrastructure. You get the performance, privacy, and cost-efficiency your applications demand.
We Match Hardware to Workload
Different AI tasks require different hardware: GPUs for training, NPUs for inference, CPUs for preprocessing. We select and configure the right hardware for your specific needs.
We Optimize for Your Environment
Edge, on-prem, or cloud—we deploy AI where it makes the most sense for your latency, cost, and privacy requirements. No one-size-fits-all solutions.
We Provide Full Support
From hardware selection and procurement to deployment, monitoring, and maintenance. We're your partner for the entire AI infrastructure lifecycle.
We Future-Proof Your Investment
Open-model ready hardware and flexible architectures ensure your AI infrastructure can evolve as models and requirements change. No proprietary lock-in.
Why this matters
Generic AI breaks down when the work depends on your systems, rules, and private context.
A demo can look impressive while still failing the real operation. Useful custom AI has to fit the workflow, handle exceptions, protect access, and make human responsibility obvious.
Simple automation stops when inputs vary, context is missing, or an approval decision matters.
Teams lose confidence when an AI output appears without evidence, ownership, or a correction path.
How the solution works
Fit the system to the workflow—not the workflow to an AI demo.
Client-owned assetsHuman approval gatesInspectable handoffNo outcome guarantees
Choose one use case, baseline, approval boundary, and result the team can observe.
Give the system only the tools and context it needs, with least-privilege access.
Test normal work, edge cases, failures, and handoffs before expanding the scope.
Why Local
When the cloud isn't the answer
Data Boundary
Keep selected workloads closer to the systems and people responsible for them, with access and retention documented.
Latency Choices
Evaluate on-site inference when network dependency or response time is part of the real requirement.
Cost Visibility
Compare hardware ownership, model serving, updates, monitoring, and support against the cloud alternative.
Model Choice
Evaluate open and hosted models against quality, maintenance, licensing, and operating constraints.
What We Deploy
From edge devices to GPU clusters
On-Prem LLM Deployment
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
Edge AI Hardware
Inference at the network edge for real-time, offline-capable workloads.
- NVIDIA Jetson & Coral TPU
- Custom edge nodes
- Vision & sensor inference
- Low-latency deployment
Managed & Supported
We don't hand you a box — we hand you a working, monitored system.
- Hardware selection & procurement
- Monitoring & alerting
- Updates & security patches
- Ongoing support
FAQ
Local AI infrastructure — questions
Why run AI locally instead of the cloud?
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.
What hardware do you deploy?
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.
Can you run open models on-prem?
Yes. We deploy and optimize open-weight LLMs (Llama, Mistral, Qwen and others) locally with quantization and serving tuned for your hardware.
Do you handle setup and maintenance?
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.
Make the next decision
What should be automated, and what should stay deterministic or human-led?
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?