What does sovereign AI mean?

It means your organization controls the data, the model, the infrastructure and the keys behind your AI, in the jurisdiction you choose, instead of depending on a service whose terms and location someone else sets.

Do we need to fine-tune a model?

Not always. Many tasks work well with a general model grounded in your own documents. We test retrieval, fine-tuning and distillation against your cases and recommend the lightest approach that meets the bar.

Who owns the fine-tuned model?

You do, subject to the licence of the base model, which we check with you before we start. You receive the adapters or weights, the training recipe and the evaluation set. We do not use your data to train anyone else's model.

Which models do you start from?

Open-weight and licensed models for language, vision and speech, chosen for your task, languages and licence needs. We recommend after testing on your own cases.

Sovereign AI | The Boltcliq Company Solutions

01Overview

What we do

Most organizations use AI the way they use electricity: through a meter they do not control. Prompts, documents and customer records travel to a provider's servers, the model behind the service changes without notice, and the bill moves with someone else's pricing. For a bank, a ministry, a hospital or a telecom, that is a risk to manage, not a foundation to build on. Sovereign AI is the alternative: models you own, trained on data you control, running where you decide.

The work has two halves. The first is making a model that is genuinely yours. We start from strong open or licensed base models, then adapt them to your language, your documents, your products and your rules. Depending on the problem, that means retrieval over your knowledge, supervised fine-tuning, parameter-efficient adapters, distillation into smaller and cheaper models, or a combination. We evaluate every step on your own cases, so you can see where a custom model beats a general one and where it does not.

The second half is deployment. A model is only sovereign if you control where it runs. We deploy fine-tuned models on your premises, including air-gapped sites, inside your own cloud account, on Boltcliq-managed private cloud, or with a local cloud or data centre provider you choose. The same model and the same controls move between these options, so a choice made today does not lock you in tomorrow.

02

Four things you can own

Sovereignty is not a single switch. It is four separate questions, and each can be answered differently. Who owns the data used to adapt the model? Who owns the resulting weights? Who controls the infrastructure it runs on? And whose law and whose keys govern it?

We design the answer to each, write it down, and build it so it can be checked. Which obligations apply depends on your sector and countries, so we work alongside your counsel and do not give legal advice.

Data you control end to end
Model weights and adapters you can take with you
Infrastructure in a location you choose
Keys, access and audit under your policy

03What you get

Capabilities

Fine-tuning on your own data

We adapt open and licensed models to your domain using supervised fine-tuning and parameter-efficient methods. Training data is prepared, cleaned, de-identified where needed and versioned. Training runs inside your boundary, so raw data does not leave it. You receive the adapters, the training recipe and the evaluation report.

Data preparation and de-identification
Fine-tuning and adapter training
Reproducible training recipe and reports

Fine-tuning on your own data

We adapt open and licensed models to your domain using supervised fine-tuning and parameter-efficient methods. Training data is prepared, cleaned, de-identified where needed and versioned. Training runs inside your boundary, so raw data does not leave it. You receive the adapters, the training recipe and the evaluation report.

Data preparation and de-identification
Fine-tuning and adapter training
Reproducible training recipe and reports

A model that speaks your terms makes fewer costly mistakes.

Retrieval and knowledge grounding

Not every problem needs training. Often the right answer is a general model grounded in your documents, records and ontology at question time. We build retrieval over your knowledge with access controls, citations and freshness rules, and combine it with fine-tuning when the evidence says it helps.

Retrieval with citations and permissions
Ontology-aware context through Zeus
Fine-tuning only where it earns its cost

Retrieval and knowledge grounding

Not every problem needs training. Often the right answer is a general model grounded in your documents, records and ontology at question time. We build retrieval over your knowledge with access controls, citations and freshness rules, and combine it with fine-tuning when the evidence says it helps.

Retrieval with citations and permissions
Ontology-aware context through Zeus
Fine-tuning only where it earns its cost

Fresh, traceable answers without retraining every week.

Small, efficient and local-language models

Bigger is not always better. We distil and quantise models to run on modest hardware, including a single server or an edge site, and adapt them for the languages your customers actually speak. This cuts cost, reduces latency and makes on-premises deployment practical.

Distillation and quantisation
Local-language adaptation and evaluation
Right-sized models for each task

Small, efficient and local-language models

Bigger is not always better. We distil and quantise models to run on modest hardware, including a single server or an edge site, and adapt them for the languages your customers actually speak. This cuts cost, reduces latency and makes on-premises deployment practical.

Distillation and quantisation
Local-language adaptation and evaluation
Right-sized models for each task

The cheapest model that does the job is the sovereign one.

Evaluation you can trust

We build an evaluation set from your real cases and use it to compare base models, tuned models and prompts. We test for accuracy, refusals, bias, leakage of private data and behaviour in local languages. Results are repeatable, so you can re-run them whenever the model or your data changes.

Task-specific evaluation sets
Safety, bias and leakage tests
Regression checks on every release

Evaluation you can trust

We build an evaluation set from your real cases and use it to compare base models, tuned models and prompts. We test for accuracy, refusals, bias, leakage of private data and behaviour in local languages. Results are repeatable, so you can re-run them whenever the model or your data changes.

Task-specific evaluation sets
Safety, bias and leakage tests
Regression checks on every release

You cannot own what you cannot measure.

Deployment where you decide

We deploy to four kinds of place. On your premises, including air-gapped sites. In your own private cloud account. On Boltcliq-managed private cloud, single tenant. Or with a local cloud or data centre provider you choose. Argus provides the portable runtime, so the model, its controls and its monitoring move between them.

On premises and air-gapped
Your private cloud account
Boltcliq-managed private cloud
Local provider or data centre

Deployment where you decide

We deploy to four kinds of place. On your premises, including air-gapped sites. In your own private cloud account. On Boltcliq-managed private cloud, single tenant. Or with a local cloud or data centre provider you choose. Argus provides the portable runtime, so the model, its controls and its monitoring move between them.

On premises and air-gapped
Your private cloud account
Boltcliq-managed private cloud
Local provider or data centre

Location is a policy decision, not a vendor default.

Governance, keys and audit

Every model call is authenticated, authorised and recorded. Odin issues identities and credentials, Monolith sets what each agent and model may see and do, and Cyclopes keeps the evidence. Encryption keys are held by you. Prompts, outputs and logs follow the same placement rules as the data they came from.

Customer-held keys
Per-agent permissions and approvals
Audit trail for every model call

Governance, keys and audit

Every model call is authenticated, authorised and recorded. Odin issues identities and credentials, Monolith sets what each agent and model may see and do, and Cyclopes keeps the evidence. Encryption keys are held by you. Prompts, outputs and logs follow the same placement rules as the data they came from.

Customer-held keys
Per-agent permissions and approvals
Audit trail for every model call

Sovereignty without control is only a location.

04African sectors

Where it applies

01

Situation

A bank wants an assistant for credit analysts and customer service, but customer records cannot be sent to a foreign-hosted model, and regulators ask where the model runs.

02

What it does

We fine-tune a model on de-identified internal documents and deploy it inside the bank, on premises or in its private cloud, with keys held by the bank and every call logged.

03

Human decides

Risk and compliance decide which tasks the model may handle and what needs human approval. Counsel confirms the regulatory position.

05Boltcliq platform

Built on

Boltcliq product

Argus

Provides the portable runtime and private data platform where the model runs and its context is stored.

The model can move between sites without rebuilding.

Boltcliq product

Monolith

Governs what the model may see, remember and do, and routes calls through one controlled gateway.

A sovereign model is also a governed one.

Boltcliq product

Odin

Issues identities and credentials, enforces access and holds the policy decisions for people and models.

Keys and access stay under your policy.

Boltcliq product

Cyclopes

Records inputs, outputs, cost and quality so every model call can be explained and re-tested.

You can show a regulator what the model did.

06

What we do and do not do

Does

Fine-tune, distil and evaluate models on your data
Deploy on your premises, your private cloud, Boltcliq cloud or a local provider
Hand over weights, adapters, recipes and evaluation sets
Design key control, access and audit with your counsel
Train your operators to run and improve the model

Does not

Train other customers' models on your data
Claim a tuned model always beats a general one without testing
Guarantee GPU supply or a specific chip
Give legal advice on data protection law
Name providers or partners without an agreement in place

07FAQ

Questions we hear

It means your organization controls the data, the model, the infrastructure and the keys behind your AI, in the jurisdiction you choose, instead of depending on a service whose terms and location someone else sets.

08

Our team of consultants and experts brings a combined 35 years of industry experience.

Immersed in technology and across many different verticals, so the people on your project have done this before.

Sovereign AI

Renting intelligence is not owning it. Your data, your model and your infrastructure should answer to you, in your jurisdiction, under your keys.

What we do

Four things you can own

Capabilities

Fine-tuning on your own data

Fine-tuning on your own data

Retrieval and knowledge grounding

Retrieval and knowledge grounding

Small, efficient and local-language models

Small, efficient and local-language models

Evaluation you can trust

Evaluation you can trust

Deployment where you decide

Deployment where you decide

Governance, keys and audit

Governance, keys and audit

See how this applies to your organization.

Where it applies

Built on

Argus

Monolith

Odin

Cyclopes

What we do and do not do

Questions we hear

What does sovereign AI mean?

Do we need to fine-tune a model?

Who owns the fine-tuned model?

Where can the model run?

Which models do you start from?

Related solutions

Neo Cloud & Inference

Data & Cloud

AI Factories

Our team of consultants and experts brings a combined 35 years of industry experience.

Ready for Sovereign AI?

Ready for Sovereign AI?