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AI that stays inside your environment — when the cloud isn't an option.

n8n workflow automation, local LLM deployment, and self-hosted agent frameworks for privacy-critical, compliance-bound, or IP-sensitive workloads. The same engineering rigor we bring to Microsoft AI, applied to open tooling — because the right answer isn't always a cloud API.

The problem we solve

Recognize your situation?

Your data can't leave your environment

GDPR data-residency, HIPAA, IP-sensitive R&D, or air-gapped operations. Cloud LLMs are off the table. Local LLMs — properly deployed — give you the capability without the data movement.

Cloud LLM costs scaling unsustainably

Per-token pricing works at low volume; at scale it breaks the model. Local LLMs shift cost from variable to fixed and let you right-size hardware for your real workload.

Microsoft-only shops hitting a capability ceiling

Some use cases need models or workflows Microsoft's stack doesn't serve well — long-context analysis, specialized fine-tunes, specific open-weights models. Open tooling fills those gaps without ripping out your Microsoft investment.

What's included

What an engagement covers

What we build on open and local AI — production-grade, not science projects. We run open agent stacks internally for our own operations; we can deploy and configure the same for yours.

  • n8n workflow automation
  • Local LLM deployment (Ollama, vLLM)
  • Model selection and tuning (Llama, Mistral, Qwen)
  • Hybrid local + cloud LLM routing
  • Self-hosted open agent frameworks
  • MCP server setup for any stack
  • Hardware guidance and right-sizing

Related use cases

If this sounds like you

“Our data can't leave our environment”

Local LLM deployment for privacy, compliance, or IP-sensitive workloads — without losing AI capability.

FAQ

Questions about this work

When is local actually cheaper than cloud?
Above a workload-dependent break-even point — typically when you're running enough inference volume that monthly cloud API spend would fund hardware amortization in 12–18 months. We model the actual break-even for your workload during scoping; we won't quote a number without that context.
What hardware do we need?
Scope-dependent — and we won't promise specs without a discovery call. The right answer ranges from a single workstation GPU for small workloads to a multi-GPU server for team-scale inference. We right-size to your actual volume, latency, and concurrency requirements.
Can we start hybrid (some cloud, some local)?
Yes, and it's often the right starting point. Routing logic sends privacy-sensitive queries to local models and high-volume commodity queries to cloud — you get the cost benefits of cloud where it fits and the privacy of local where it matters.
How do you keep open-source models maintained and patched?
Model lifecycle is a first-class concern, not an afterthought. We set up a review-and-update cadence, vulnerability monitoring, evaluation regression tests, and a clear ownership model so the deployment doesn't go stale after handover.

Other practice areas

Explore the rest of what we do

01

D365 F&O Implementation & ERP Modernization

Modernize legacy ERP to Dynamics 365 F&O — full-lifecycle delivery from architecture through go-live and beyond.

02

Microsoft AI

Agents, Copilot extensions, and automation on Microsoft's AI stack — built to fit how your team works inside M365 and D365.

03

Azure Integration Services

The connective tissue between your systems — built on Azure to hold up as everything around it changes.

04

Strategy & Advisory

Honest readiness assessments and clear roadmaps — before you commit budget.

Jump to section

Start with a discovery call.

30 minutes, no obligation. You leave with a clear next step — even if that step isn't us.

Book a discovery call