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On-premise & sovereign AI

AI that never leaves your network.

For organisations that cannot send data to a cloud AI service: models, data and processing running entirely inside your own boundary, including fully air-gapped.

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Service overview

Sovereign AI means the whole path stays inside a boundary you control: the data at rest, the inference, the orchestration and the audit store. No third-party AI endpoint sees your records, and in air-gapped deployments there is no egress at all.

It matters where regulation, classification or contract makes cloud AI services simply unavailable: government, defence, healthcare, banking and critical infrastructure. It also matters when the commercial risk of a data incident outweighs the convenience.

The trade is real and we are direct about it: self-hosted open weights lag frontier models on the hardest reasoning tasks, and you carry hardware. For most production workflows, such as extraction, classification, routing, summarisation and retrieval, that gap does not decide the outcome. Where it does, we tell you before you buy GPUs.

Talk to us

Need it inside your own boundary?

Tell us the constraint, whether regulatory, contractual or classification, and we will tell you honestly what is achievable on your own infrastructure.

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What we build

Government and public sector

Classified or citizen data that cannot leave national infrastructure, with evidence for the reviewing body.

Defence and critical infrastructure

Fully air-gapped deployments, with weights and updates arriving through your controlled-media process.

Healthcare

Patient records processed inside your own estate, with retention and access to your policy.

Banking and finance

Client data kept in-tenancy, with a full audit trail per automated decision.

Legal and IP-sensitive work

Contract and disclosure review where the material cannot be sent to a third party.

Data residency requirements

Region-pinned processing where the law dictates where computation happens.

On-premise inference clusters

Self-hosted open weights on your own hardware, sized against real concurrency.

Private RAG over internal documents

Retrieval across your estate where nothing may be embedded outside the boundary.

PII minimisation and redaction

Tokenisation before inference wherever the task does not need the raw field.

Security review evidence

Topology, access model and audit format documented as a pack for your reviewers.

Offline model updates

A repeatable process for moving new weights inside without opening egress.

Sovereign incident response

Runbooks and on-call that work when the system cannot phone home.

Why Momentem for this

About the company →
0
bytes of egress in an air-gapped deployment

Not a policy promise. In these topologies there is no third-party endpoint to send data to in the first place.

No training on your data, structurally

Enforced by the deployment topology rather than a clause in a contract.

Honest sizing

Hardware, quantisation and throughput modelled against your real volumes, with the performance gap stated up front.

Evidence your reviewers accept

Topology, access model, audit format and retention documented as a pack, not assembled after the fact.

Fully reversible operations

Every action logged with actor, cost and outcome, and undoable in one step by your own team.

Residency you control

Region-pinned or wholly on-premise, so computation happens where the law says it must.

Updatable without egress

Model and dependency updates arrive through your existing controlled-media process.

On-site where required

For classified or air-gapped work our engineers work inside your facility for the duration.

How this compares

Momentem
Large consultancy
Off-the-shelf tool
Where inference happens
Inside your boundary
Usually their cloud
Their cloud
Air-gapped support
Yes, no egress
Rare
No
Model choice
Open weights you control
Vendor-selected
Vendor-selected
Audit evidence
Documented pack
Available on request
Trust the SOC report
Hardware
Yours, sized with you
Their infrastructure
Not applicable
Data leaving the estate
Never, by design
Contractually restricted
By default

Based on publicly available information and our own experience of comparable engagements. Generalisations rather than claims about any specific provider. There are good exceptions in every column, and the point is where each model is structurally strong.

The stack we work in.

Open models

Weights you host yourself, sized honestly against the tasks you actually need them to perform.

metamistralaihuggingfacegooglegeminiollamapytorchmetamistralaihuggingfacegooglegeminiollamapytorch

Serving

Inference on your own metal, entirely inside the boundary your policy or regulator defines.

nvidiadockerkuberneteslinuxpythonraynvidiadockerkuberneteslinuxpythonray

Hardware

Sized with you rather than sold to you. We would rather right-size than oversell capacity.

nvidiaamdintelsupermicrodelllinuxnvidiaamdintelsupermicrodelllinux

Controls

The evidence your security review will ask for, documented as a pack before you need it.

vaultoktaopentelemetryelasticgrafanaprometheusvaultoktaopentelemetryelasticgrafanaprometheus

How the build runs.

Week 0

Scope

Sessions with the people doing the work. We leave with a written target and a data map.

Week 1 to 2

Prove

A thin slice against your real records, scored on a test set drawn from your own history.

Week 3 to 12

Deploy

Integrations, approval gates, audit logging. Live on one team with a rollback switch.

Ongoing

Hand over

Runbooks, paired on-call and a decision log, until your team changes it without us.

A padlock closed on a steel equipment cabinet
Six weeks, in ninety seconds.Walkthrough · 1:30

Tell us what your boundary has to be.

Forty-five minutes with the people who would build it. No pitch, and a straight answer on whether it is worth building.

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Questions

All questions

For extraction, classification, routing, retrieval and summarisation, usually yes. For the hardest reasoning tasks there is still a gap, and we will tell you where your workflow sits before you commit to hardware.

Yes. No egress at all, with model weights and dependency updates moving through your existing controlled-media process.

It depends on concurrency and latency targets. Sizing is part of the engagement, and we would rather right-size than oversell GPUs.

Yes. We produce the topology, access model and audit documentation, and we answer questionnaires ourselves. Engineers, not a compliance mailbox.

Yes, and that is a common path. The orchestration layer is the same; what changes is where inference runs.

Explore other solutions

Custom AI platforms

Agent systems and intelligent workflows built around your exceptions

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Forward deployment

Senior engineers embedded inside your team until it ships

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Software & internal platforms

Operator consoles, portals and the infrastructure underneath

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Data & integrations

Reversible write-back into the systems you already run

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AI strategy & audits

Where AI fits, what it is worth, and what to build first

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