On-premises AI infrastructure means your AI tools, language models, and automation workflows run on hardware inside your own network rather than on public cloud platforms operated by third parties. For businesses that handle sensitive data – patient records, financial information, donor data, defense contracts – this distinction is critical. When you use a public AI service, your data may be transmitted to and processed on external servers outside your control. On-prem AI keeps that data inside your network where your existing security controls, access policies, and compliance frameworks apply.

Not without careful controls in place. Public AI platforms process your inputs on their own infrastructure, and depending on the platform’s data retention and training policies, sensitive information entered into a prompt may be stored, reviewed, or used to improve the model. For businesses subject to HIPAA, CMMC, PCI DSS, or GLBA, submitting protected data to a public AI service can create a compliance violation – even unintentionally. Before recommending any solution, we conduct an AI tool audit to identify what platforms your staff is currently using and where data exposure risks exist. That audit informs whether on-prem AI is the right path and what controls need to be in place.

On-premises AI runs on hardware physically located in your facility or a data center you control. Private cloud AI runs on dedicated cloud infrastructure isolated from other tenants but still operated by a third-party provider. Both approaches keep your data out of public, multi-tenant AI platforms. The right choice depends on your compliance requirements, budget, existing infrastructure, and tolerance for managing hardware. We help you evaluate both options during the assessment process and recommend the approach that fits your situation.

We currently support Ollama for local model serving, Llama-based open-source language models, and Claude Code for agentic development workflows. We design infrastructure to host these platforms securely on your network, configure access controls and integrations, and manage the ongoing maintenance of the AI stack. As the on-prem AI ecosystem evolves, we expand our supported platforms accordingly.

n8n is an open-source workflow automation platform – similar in concept to Zapier or Make, but designed to be self-hosted so your data never leaves your network. It connects your business applications, databases, and AI tools through automated workflows that run entirely inside your infrastructure. We deploy self-hosted n8n instances for clients and also migrate businesses from n8n cloud deployments onto self-hosted environments for improved data control. Current engagements focus primarily on internal productivity workflows – automating repetitive processes so staff can focus on higher-value work.

Both. We design and manage the infrastructure, but we also build the AI agents, skills, and guardrails that run on it. This includes creating the automation workflows in n8n, configuring AI agents to perform specific business tasks, applying guardrails to control what the AI can access and how it responds, and migrating existing cloud-based workflows onto your self-hosted environment. The Grants Pass Chamber of Commerce engagement is a live example – we built an AI agent that handles newsletter delivery end to end, running entirely on infrastructure we designed and manage.

Not always. Many business AI use cases – document summarization, internal search, workflow automation, chatbots – run effectively on CPU-based servers, especially with modern efficient models. GPU-equipped hardware becomes relevant when you need faster inference on larger models or plan to scale AI usage significantly. We procure both standard and GPU-equipped server hardware for on-prem AI deployments based on your actual requirements. We also evaluate whether your existing hosting environment can support AI workloads through virtual machines and containers before recommending new hardware purchases. You will not be sold hardware you do not need.

We procure hardware for AI deployments, including GPU-equipped servers when the use case requires it. We handle vendor selection, sizing, and procurement based on your workload requirements and budget. If you have existing server infrastructure with available capacity, we also evaluate whether that environment can support AI workloads through virtualization and containerization before recommending additional hardware. Either way, the goal is a right-sized deployment – not an oversized one.

Yes. Integration with your existing systems is a core part of the deployment. Through n8n and direct API connections, on-prem AI can connect to your business applications, databases, document repositories, email platforms, and other data sources. We handle the identity and access integration – tying AI systems into Active Directory or Entra ID, enforcing single sign-on, and applying least-privilege access policies so AI tools can only reach the data they are authorized to use.

Yes, when configured correctly. For healthcare clients, we configure AI infrastructure to meet HIPAA technical safeguard requirements – including encryption, access controls, audit logging, and activity visibility for any AI system that touches protected health information. For defense contractors, we align AI infrastructure with CMMC and NIST SP 800-171 requirements for systems that may interact with controlled unclassified information. Compliance alignment is built into the design and deployment process, not added as an afterthought.

The assessment follows the same structured approach as our managed IT onboarding process. We review your current environment, document your existing infrastructure, identify what AI tools your staff is already using and where data exposure risks exist, and evaluate your workload requirements and compliance obligations. From that review we provide a clear plan for deploying on-prem AI and automation infrastructure – including hardware recommendations, platform selection, integration requirements, and a project scope. There is no cost to the initial assessment.

The initial deployment – hardware procurement, infrastructure design, platform installation, configuration, and workflow migration – is scoped and billed as a project. Pricing depends on the complexity of your environment and the scope of what is being deployed. Ongoing management of the AI infrastructure is included as part of your managed IT services contract, the same way server management is handled. You are not paying a separate monthly fee for AI infrastructure monitoring and maintenance – it rolls into your standard managed IT plan.

On-prem AI infrastructure is designed to grow with you. We size your initial deployment based on current and near-term projected usage, but the architecture accounts for expansion. Adding compute capacity, storage, or additional AI workloads is a structured process rather than a disruptive rebuild. We review AI infrastructure utilization as part of ongoing managed IT account reviews and flag capacity needs before they become performance problems.

The clearest fit is any business that handles sensitive or regulated data and wants to use AI tools without the compliance risk of public platforms. Healthcare practices that want AI-assisted documentation or patient communication without exposing PHI. Accounting and financial services firms that want AI-assisted data analysis without sending client financials to a public model. Defense contractors who need AI tooling that does not conflict with CMMC obligations. Nonprofits that want to automate donor communications and reporting while keeping donor data in-house. If your business data is sensitive and your current AI usage involves public SaaS tools, an on-prem assessment is worth having.

Schedule your AI Infrastructure Assessment using the form on this page. We will review your current environment, audit your existing AI tool usage, identify any data exposure risks, and come back with a clear deployment plan. There is no cost to the assessment and no obligation to proceed.