Private AI
Customer records, regulated data, source code and trade secrets are often where AI would add the most value, and they are the information your business cannot hand to an outside AI company. Private AI runs the model inside your boundary, under rules you set. It is a deployment option for the workloads that warrant it, not a stage every engagement has to include.
The technical name is a local LLM inference engine. In plain terms, the AI runs on infrastructure you control instead of in someone else’s cloud.
Your boundary
Logging and retention set by your policy
Only where your policy allows
You decide
where each class of data may go
4 options
Your data centre, dedicated, air-gapped or hybrid
Agreed first
Access, support, recovery and exit, before deployment
01What you are trying to do
The board wants AI adopted, and your risk team wants nothing sensitive sent to a public AI service. Both positions are reasonable, and both can be met.
02What gets in the way
A pilot can be cheap while the full rollout is not, because cloud AI costs rise with usage. A self-hosted model set up without ongoing oversight has a different problem: its answers can slowly get worse until people stop relying on it.
03How we help
Technically, this is a local inference engine on open foundations, operated by us or by your team, with the testing and guardrails that check its quality over time.
01 / What it is
It gives your people and automated processes an AI they can use on sensitive information without that information leaving your control. Underneath is an open-weight language model, chosen for your business and served inside your boundary, behind a policy gateway that decides where each request may go. A request reaches a commercial model outside the boundary only where your policy allows it.
What makes it more than a model on a box is the service around it. We size and deploy the hardware, integrate it with the workflows that use it and, where you want us to, run it: patching, model updates, capacity, monitoring, and the evaluation harness that shows whether quality has slipped.
It is a deployment and operating option, used where a workload warrants it. Governance decides what AI is allowed to do. Private AI decides where sensitive work is processed. Many governed automation projects need only approved commercial models. Others run their sensitive steps here and the rest elsewhere.
The classes and routes are set with you in the assessment. Some businesses route everything privately, and some run hybrid.
02 / Why businesses consider it
Control depends on where the model is hosted, who has operational access and which jurisdiction applies, and each is settled separately. Cost depends on the workload. Quality depends on how the model is run after launch.
Sensitive steps run on private inference inside your boundary: your data centre, dedicated infrastructure we run for you, or an air-gapped environment. You decide who can use it, which models are permitted, where each class of data may go and which changes are approved. Those decisions are written down, enforced by policy and recorded, so you can show they were followed.
Private inference is not billed per token by an outside AI provider, so wider adoption does not arrive as a growing usage invoice from one. Capacity, operation and support still cost money. What you pay, and what it includes, is set out in the proposal before you commit, and where it is relevant the AI Workflow Assessment compares it with commercial APIs on a representative workload.
The real work in running your own AI is keeping its answers accurate after launch. Every change to the model, prompts or data is scored against an evaluation harness built for your use case before it reaches production, and you can see the results.
The same private infrastructure can serve more than one process. Service answers, case triage, document processing and compliance review can run on it, under the same authority, policy and evidence. The integration, evaluation and capacity each new workload needs determine what it costs to add.
What we automate03 / How we deliver and run it
Getting a model running on a server is quick. Keeping it accurate, available and defensible in production is the ongoing work, and it is where our senior engineers spend their time.
We size the hardware to your workload, install the inference engine inside your boundary, and integrate it with the systems that will consume it through governed APIs. In your data centre, on dedicated infrastructure we run for you, or air-gapped where the workload requires it.
Where you want us to run it, we operate the engine as a managed service, handling patching, model updates, capacity and the routine operational work that keeps a self-hosted model available. Your team gets a working service instead of another system to maintain. Support coverage and response targets are agreed per engagement.
Every change is measured against an evaluation harness built for your use case, so a model update or prompt change is scored before it can quietly degrade output. You can see the results.
Guardrails, monitoring and drift detection with named senior owners and defined response paths. This is the work that lets the business depend on a locally run model.
This is an Artrilogic capability, delivered with partner support for hardware sourcing and platform build. You deal with one accountable senior team, and what each party looks after is agreed before deployment.
04 / Before deployment
Each deployment is different, so these points are settled with you for each engagement before anything is installed.
05 / Deployment and models
We deploy in your data centre, on dedicated infrastructure we run for you, fully air-gapped for the most sensitive workloads, or hybrid with approved commercial models. The right shape is a decision we make with you in the assessment, sized to the workload. Hosting location, operational access and jurisdiction are separate considerations, and we settle each one with you.
We build on open-weight models chosen for your business, so you can inspect what runs and are not tied to one model vendor’s roadmap. The model is designed to be swappable as better options ship, and your workflows integrate against a stable interface, so upgrading underneath does not mean rebuilding on top. Portability and handover depend on the agreed deliverables, licences and operating arrangements.
This pairs naturally with our air-gapped .NET modernisation work and with MCP servers that let the engine reach your existing systems safely.
Common questions
Three reasons usually come together: data that cannot leave your boundary for regulatory or contractual reasons, costs that become hard to predict as adoption grows on a per-token API, and a need to control exactly which model version answers instead of inheriting a vendor's silent updates. When two of those are true, local inference is worth assessing seriously.
Not automatically. For the scoped, well-defined tasks most automated processes need, open models are often a good fit, and the evaluation harness shows whether they are for yours. We will tell you where a local model is the right call and where it is not yet, and we design so the model is swappable if that calculus changes.
No, and many engagements need no private deployment at all. Where one is used, most businesses route by class of data. Confidential and regulated steps run on private inference inside your boundary. Steps that only touch internal or public information can use approved commercial models through the same gateway, policy and audit. Some businesses choose to run everything privately. The choice is yours, written down and enforced.
The evaluation harness and the people watching it. We measure output quality against a suite built for your use case, so any change to the model, prompts or data is scored before it reaches production. Guardrails and automated monitoring run continuously, with named senior owners. Support coverage and response targets are agreed per engagement.
That depends on how you prefer to operate: in your data centre, on dedicated infrastructure we run for you, fully air-gapped, or hybrid. Hardware ownership or lease, hosting location and administrator access are agreed for the option you choose. Hosting location, operational access and jurisdiction are separate considerations, so we settle each one rather than assume the first answers the other two.
That is agreed before deployment, not at the end. The engine runs on open-weight models and standard interfaces, and the model is designed to be swappable. What is handed over, and on what terms, depends on the agreed deliverables, licences and operating arrangements. It does not automatically include Artrilogic's own reusable tooling.
The AI Workflow Assessment. Two weeks, fixed fee, quoted before you commit. It names the processes worth automating, classifies the data each step touches, and tells you whether private inference is the right fit and how to size it. You walk away with a written recommendation either way, including the answer that a commercial model still fits better for now.
Is it right for you?
The AI Workflow Assessment answers that. We classify the data each step touches and compare a local engine with commercial models on a representative workload, counting infrastructure, implementation, support, retries and human review, with the assumptions stated. You get a written recommendation either way.
