Why run your own AI instead of using OpenAI or Anthropic?

Three reasons: data privacy, cost at scale, and control. If your documents, client records, or proprietary knowledge cannot leave your infrastructure under any regulatory, contractual, or business requirement, a vendor-hosted model is not an option regardless of the privacy policy. At volume, on-premises inference is often significantly cheaper than API calls. The threshold depends on model size and workload, but businesses running frequent queries against large document sets often find self-hosting pays for the hardware within months. And when the model is yours, you control the update schedule, the context window, the system prompt design, and the models you run. You are not dependent on a third party's product roadmap, pricing changes, or service terms for a critical business workflow. Helix Stax runs the same private AI stack in production for its own operations.

Do you only serve Hampton Roads businesses?

No. Private AI hosting is delivered remotely and serves clients nationally. The design, deployment, and configuration work runs over SSH, VPN, and video call. There is no geographic restriction on who we work with. The nature of the work makes remote delivery practical: infrastructure is provisioned through configuration management and remote access tools, models are deployed via command-line and container workflows, and the interface your team uses is a web application accessible through a browser. Hampton Roads businesses also have access to on-site visits for initial hardware assessment and configuration sessions. For clients purchasing and racking server hardware for the first time, an on-site visit during the rack-and-stack phase is available on request.

What hardware do I need for self-hosted AI?

It depends on the model size and the workload you plan to run against it. Smaller quantized models, such as 7B to 14B parameter models at 4-bit or 8-bit quantization, run on a workstation-class NVIDIA GPU or even CPU-only on modern hardware, which works for personal productivity use cases with moderate query volume. Production inference for a team handling frequent queries or large document retrieval typically needs a dedicated NVIDIA GPU with at least 8 to 16 GB of VRAM. Larger models or multi-user deployments may require more. Before any hardware is purchased, we run a free sizing call to match the model capability you need to the hardware that makes sense for your budget and workload. Buying a GPU underpowered for your use case is a common and avoidable mistake.

Can the self-hosted AI connect to our documents and knowledge base?

Yes. Retrieval-augmented generation (RAG) pipelines connect the private model to your documents, runbooks, internal wikis, or structured data. The model does not need to be retrained on your content. Instead, relevant documents are retrieved at query time and injected into the model's context, so it answers based on your actual data. We build and operate the full RAG stack: the LLM running under Ollama, the vector database for document embeddings (pgvector or Chroma depending on your infrastructure), the ingestion pipeline that processes and indexes your documents, and the Open WebUI or custom front end your team uses to interact with the system. The ingestion pipeline handles common document formats and runs on a schedule so newly added content is available without manual intervention.

Service · Private & Self-Hosted AI Hosting

Private AI hosting for businesses whose data cannot leave the building.

Most AI tools work by sending your data to someone else's model. For businesses handling client information, proprietary knowledge, or regulated data, that trade-off is not acceptable. We deploy Ollama, Open WebUI, and private LLM stacks on infrastructure you control, so your documents stay yours.

Helix Stax deploys and operates private AI infrastructure for businesses that need AI capabilities without sending data to a vendor model. The work covers the full stack: LLM selection and deployment via Ollama, Open WebUI for team access, vector database setup for RAG pipelines, n8n workflow integration, and ongoing operations. The result is an AI tool your team uses daily, on infrastructure you own and control.

We run this stack in production for our own operations. Ollama, Open WebUI, pgvector, and n8n AI agent workflows are active in the Helix Stax platform today. When we recommend a model or an architecture, the reason is that we operate it. Private AI hosting is delivered remotely and serves clients nationally, not just Hampton Roads.

Code and AI model output on a laptop screen during a private inference deployment

Key service areas

What the work looks like.

LLM selection and deployment: open-source model scored against your use case, hardware, and data-privacy requirements, installed and tested before your team touches it
Ollama setup and configuration: model management, quantization selection, and GPU or CPU inference tuned to your hardware
Open WebUI deployment: team-facing chat interface with user management, conversation history, and model switching
RAG pipeline build: retrieval-augmented generation against your documents, runbooks, or knowledge base using pgvector or Chroma
n8n AI workflow integration: private AI models wired into your existing workflows and automation stack
Ongoing operations: model updates, hardware monitoring, and capacity planning inside a managed retainer

Named engagements inside this capability

How this shows up as a scoped engagement.

Ollama & Open WebUI Deployment

The fastest path to a working private AI stack. We install Ollama, select the right quantized model for your hardware, deploy Open WebUI for team access, and configure user management. The result is a working tool your team can use the same day, not a proof of concept.

Hardware assessment: CPU vs GPU inference, VRAM requirements, and a sizing recommendation before any purchase
Model selection: open-source LLM scored against your use case (general chat, code assist, document summarization) and your hardware
Ollama installation and configuration on Linux, with systemd service, automatic restart, and basic monitoring
Open WebUI deployment with HTTPS, user accounts, model access controls, and conversation logging

RAG Pipeline & Knowledge Base

A RAG pipeline connects your private AI model to your documents, runbooks, or structured data, so the model answers questions about your business, not just the public internet. We build the ingestion pipeline, configure the vector database, and integrate the retrieval layer into Open WebUI or your custom front end.

Document ingestion pipeline: PDFs, Word documents, Markdown, and web content processed and stored as vector embeddings
Vector database setup: pgvector on PostgreSQL or Chroma, configured for your document volume and query patterns
Retrieval integration: the vector search wired into the model prompt so answers cite the relevant document and section
Maintenance runbook: the process for adding new documents, re-indexing changed content, and monitoring retrieval quality

AI Workflow Integration

A private AI model that only lives in a chat interface misses most of the value. We wire the model into your n8n workflows so it can summarize inbound emails, classify support tickets, draft responses, or process documents as part of an automated pipeline, without sending data to a vendor.

n8n AI node configuration: Ollama API wired into existing or new n8n workflows with error handling and retry logic
Use-case build: one documented workflow connecting the private model to a real business process (ticket triage, email summarization, document extraction)
Prompt library: the prompts that produce reliable output for your use case, documented and version-controlled
Monitoring and alerting: workflow failure alerts and basic model health checks so silent failures get caught

How we engage

Scoped deployment or ongoing operations.

Private AI hosting runs as a scoped Engagement (install and hand off) or as an ongoing Operate retainer (we manage the infrastructure, you use the tool).

vCIO Retainer

Advisory on which AI use cases fit a private stack, which fit a vendor API, and which are not ready for either. We help you make the build-vs-buy call with real numbers, not vendor slides.
Helix Engagement

We install the stack, configure the model, build the first RAG pipeline or workflow integration, and hand off a documented environment. You own the hardware and the configuration.
Helix Operate

Ongoing management of the private AI stack: model updates, hardware monitoring, capacity planning, and new workflow builds as your use cases grow. The AI is a managed layer, not a one-time project.

What you walk out with

Concrete deliverables.

A hardware sizing recommendation: CPU vs GPU inference, VRAM requirements, and the specific hardware we recommend before any purchase
A running Ollama and Open WebUI stack with HTTPS, user accounts, and the right model for your use case
A RAG pipeline against your document corpus with a tested retrieval quality check
One documented n8n AI workflow connecting the private model to a real business process
A deployment runbook: how the system was built, how to add models, how to add documents, and how to restart services after a failure

Honest scope

What we do not do.

We do not fine-tune or pre-train foundation AI models. We deploy and operate existing open-source models. We do not guarantee model accuracy or output quality, no one can do that honestly. We do not build full custom AI applications from scratch at the infrastructure tier; scoped workflow builds and RAG pipelines are in scope but full application development is not. We do not sell GPU hardware; we size it and you purchase it. We do not offer 24/7 AI monitoring at the Engagement tier, that is Operate scope.

Industries we apply this to

Where this service shows up most.

You can have the number by Friday.

The free call is free, and the only thing you walk out with is your CTGA score and the three gaps that cost you the most. If we are not the right fit, you keep the score and we both move on.

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