You want AI in the business, but not your data on someone else's servers.
Sensitive records, regulated data, and IP you cannot expose. The board wants AI adopted. Risk wants nothing sent to a public LLM. Both are right.
Private AI · runs on your own hardware
Your private AI, on your premises and shielded.
The productivity of AI, without sending your data, your customers’ records, or your trade secrets to someone else’s servers. The AI runs inside your walls. Nothing sensitive leaves. We set it up and keep it running, so it stays private, affordable, and reliable.
The technical name is a local LLM inference engine. In plain terms, the AI runs on your computers instead of in someone else’s cloud.
$0 per-token usage fee
No cloud bill that grows every month
100% of your data on-prem
Nothing sensitive leaves the building
Self-healing built in
If a part fails it restarts and moves on, 24/7
Per-token API bills grow with every team that adopts. Nobody budgeted for that curve.
The pilot was cheap. The rollout is not. Cloud inference costs scale with success, and a self-hosted model stood up without senior oversight can quietly degrade until users stop trusting it.
Sovereign by design. Predictable in cost. Run by people who keep it correct.
A local inference engine inside your boundary, on open foundations, operated as a managed service with the evaluation and guardrails that stop it drifting.
What it is
A productised engine, delivered as a managed service.
The engine is a local, open-weight large language model, built around your business and served on private GPU hardware inside your boundary. It is wrapped in the integration, evaluation and guardrail layers that make it safe to build on. Your systems reach it through governed APIs, and nothing it processes leaves the perimeter.
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 consume it, and then run it: patching, model updates, capacity, monitoring, and the evaluation harness that proves quality has not slipped. Local inference is easy to start and hard to keep correct. The senior capability to keep it correct is what we sell.
It is the flagship of our AI practice and shares its posture. Open foundations, vendor liquidity, and an honest answer about when a local model is the right call and when a cloud model still fits better for now.
Where your data goes
Your data stays on your side of the line.
The private work happens inside your walls. Only what is meant to be public ever leaves. The big AI providers sit on the far side, and your data never reaches them.
Private and protectedOn-prem
On-prem, inside your walls
Your teamthe people
Private AIon your hardware
Your business app
Data access guardpolicy · audit
Your datanever leaves the building
Controlled
Gateway
Gateway
Public internet
Outside your walls
Public cloudAWS or Azure
Your customers
Big AI providers
Claude, OpenAI, Microsoft, Google
Your data never goes here
Why enterprises choose it
Three concerns, answered by where the box sits and who runs it.
Sovereignty, cost and quality are the three questions we hear at this scale. Local inference answers the first two by design and the third by senior capability.
Your data stays yours
The AI runs on hardware inside your walls. Your questions, your documents and its answers never leave the building and never reach an outside company. It is not a promise on a contract page. It is simply where the machine sits.
No surprise bills
There is no meter running every time someone uses it. You pay for your own hardware and a fixed monthly fee, and that is the whole cost. Finance can forecast it, cap it, and stop worrying about a runaway invoice.
It stays reliable
The hard part of running your own AI is not switching it on, it is keeping it trustworthy. We watch its answers and catch problems before your people do. It is self-healing: if something fails, the work restarts and moves to healthy hardware on its own, around the clock. Senior people own it, not a call centre.
One platform, many uses
One investment. A whole team of specialists.
Private AI is not a single chatbot. The same on-premises platform runs a growing set of specialist assistants, each tuned to a job. Add new ones without buying more infrastructure.
Help desk answers
Answers staff and customer questions from your own documents, with citations, and hands off to a person when it is unsure.
Ticket triage
Reads incoming IT and support tickets, sorts and routes them, drafts replies, and closes out the repeat ones.
Security review
Checks configurations and alerts against your own policies and drafts incident notes for your team to action.
Sales and data insight
Answers plain-language questions over your sales and operations data, and flags the anomalies worth a look.
Knowledge search
Turns scattered documents into a searchable, trustworthy answer engine for the whole organisation.
Workflow automation
Plugs into the tools and workflows you already run, so the AI does the busywork from end to end.
One platform underneath all of it. A new specialist is a configuration change on hardware you already own, not a fresh project and not another vendor bill.
How we deliver and run it
Deploy, operate, evaluate, assure.
A model on a server is a weekend project. Keeping it correct, available and defensible in production is the work. That is where the senior capability goes.
Deploy
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. On your hardware, in your data centre, or air-gapped where the workload requires it.
Operate
We run the engine as a managed service. Patching, model updates, capacity, and the boring operational discipline that keeps a self-hosted model available. Your team does not inherit a science project.
Evaluate
Every change is measured against an evaluation harness built for your use case, so a model update or prompt change cannot quietly degrade output. Quality is a number we watch, not a hope.
Assure
Guardrails, monitoring and drift detection with named senior owners and defined response paths. The senior capability that makes local inference safe to depend on is the whole point of the offering.
This is an Artrilogic capability, delivered with extended partner support for hardware sourcing and platform build. You deal with one accountable senior team, with the supply and infrastructure muscle behind it.
Deployment models
We deploy on hardware in your data centre, on private GPU infrastructure we manage on your behalf, or fully air-gapped for the most sensitive workloads. The right shape is a decision we make with you in the assessment, sized to the workload and to your sovereignty posture.
Built on open-weight models
We build on open-weight models, chosen and tuned for your business, so you can inspect them, keep them, and are never tied to one 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.
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
What enterprises ask before they run a model themselves.
Why run inference locally instead of calling a cloud LLM API?
Three reasons usually stack up together. Data that cannot leave your boundary for regulatory or contractual reasons. A cost curve that becomes unpredictable as adoption grows on a per-token API. And a need to control exactly which model version answers, rather than inheriting a vendor's silent updates. When any two of those are true, local inference is often the calmer long-term choice.
Does a local model mean worse quality than a frontier cloud model?
Not automatically, and less so every quarter. Open models have closed much of the gap for the scoped, well-defined tasks that most enterprise workflows actually need. We are honest about 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.
What stops the model from drifting or degrading over time?
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 monitoring run continuously, with named senior owners. This assurance layer is the core of the offering, not an add-on.
Whose hardware does it run on?
Yours, ours, or a hybrid, depending on your sovereignty and operational posture. We can deploy on hardware in your data centre, on infrastructure we manage on your behalf, or fully air-gapped for the most sensitive workloads. The deployment model is a decision we make with you in the assessment.
Are we locked into you once the engine is running?
No, by design. The engine runs on open foundations and standard interfaces. We document it so your team could take operation in-house, and we build so the model itself is swappable. Vendor liquidity applies to us too. That is the point of open foundations.
What is the smallest sensible first step?
An AI readiness assessment. A fixed-scope diagnostic that names your candidate workloads, tells you honestly whether local inference is the right fit, and sizes the hardware and cost if it is. You walk away with a written recommendation either way, including the honest answer that a cloud model still fits better for now.
Is local inference the right call for you?
An AI readiness assessment answers that honestly. We name your candidate workloads, size the hardware and cost, and tell you plainly whether a local engine beats a cloud model for your situation yet. You get a written recommendation either way.