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// private ai

Your AI. Your servers. Your rules.

Private AI means running generative AI entirely inside your own infrastructure: your models, your servers, your network, your data. Nothing leaves. I help organizations design, deploy, and operate these systems, from the first hardware sizing conversation to a production deployment your team runs confidently on its own. This specialty was born of necessity: when I brought AI tools into my own projects, the problems that hit me immediately were security, performance, and privacy. Solving them for myself became a practice, and that practice became the work I now do for others.

Why private AI

  • Data control: your source code, documents, and customer data never travel to a third-party API. There is no prompt log sitting on someone else’s servers.
  • Compliance: if your industry or your clients demand that data stays in-house (healthcare, finance, legal, government, defense), private deployment is not a preference, it is the requirement.
  • Predictable cost and latency: no metered API bills that scale with your team’s enthusiasm, no rate limits, no dependence on someone else’s uptime. Inference runs at the speed of your own hardware, every time.
  • Real capability: modern open models are genuinely good. For most internal workloads, a well-chosen local model on the right hardware covers the need, and the gap narrows every quarter.

What I deploy

Local model runtimes

Self-hosted inference with runtimes like Ollama and comparable serving stacks: model selection, quantization choices, GPU and memory sizing, and the operational setup to keep models updated and monitored. I published ailane, an open-source tool that inspects a machine’s hardware and reports which local AI models it can run, because sizing is the first question every deployment faces.

Private AI-assisted development

Coding assistants that work against local or self-hosted models, so your engineers get AI help without code leaving the network. This includes the workflows, review discipline, and quality control that make AI assistance an asset in production, the subject of my book You Are the Quality Control.

Internal knowledge systems

Retrieval-augmented generation over your own documents, wikis, and codebases: your institutional knowledge made queryable, without indexing any of it into an external service.

Team enablement

Training and runbooks so the deployment survives contact with real use: who updates models, how quality is evaluated, what the escalation path is when output goes wrong. A private AI system your team cannot operate is a demo, not a deployment.

How engagements work

  • Assessment: we map your use cases, data constraints, and existing hardware, and decide what private AI should and should not do for you. Sometimes the honest answer is a hybrid, and I will tell you so.
  • Pilot: a scoped deployment on real workloads with clear evaluation criteria, so the decision to go further is made on evidence.
  • Production: hardening, monitoring, model update strategy, and handover to your team.
  • Training: hands-on sessions for the engineers who will live with the system.

Private AI deployment is one part of my wider consulting practice, which also covers AI-assisted development adoption, software architecture, and cloud modernization.

Guides and articles

I document this work continuously. Start here:

New guides land here as they are published.

Start a conversation

If your organization wants the productivity of generative AI without handing its data to a third party, tell me about your situation: contact me here.