Sovereign AI, operationalized

Truly Sovereign AI - On your terms.

Sovereignty is not just local hosting. It is freedom of action - deciding what changes, when, and on whose terms, as AI evolves.

Control LLMs.Move workloads.Protect knowledge.Control economics.
The gap

AI demos are easy. Industrial AI is hard.

Capability discovery

Demo world

Fast promptsImpressive responsesCloud APIsLow initial commitment

Weightless. Everything floats - nothing is load-bearing yet.

Operational responsibility

Production world

  • ROI pressure
  • Security boundaries
  • System integration
  • Predictable cost at scale

Load-bearing. Every line here can stop a rollout.

Inside the gap

When AI speed hits enterprise stability: three control failures hit hard.

LLMs get retired on the provider's schedule

New models ship weekly and old ones get switched off. Validated solutions are dragged along at the provider's pace.

Shared capacity runs out when you need it most

AI demand doubles in months; data centers take years to build. Shared capacity rations the gap, and your key use cases feel it.

Unit economics get reset upstream

Provider pricing and terms reprice in weeks. Budgets and business cases are set annually.

The answer

A decoupling layer between AI speed and enterprise stability.

Adopting agentic AI is inevitable. Absorbing its volatility directly into the business is neither sustainable nor safe.

AI innovation - changing in days to weeks
AI Intime - the decoupling layer
  1. Sandbox new tech rapidly. New models and frameworks land in a sandbox, never in production.
  2. Test against your evaluations. Your business test cases decide whether the new tech is better.
  3. Adopt where value is proven. Roll out on your schedule. Nothing changes without your decision.
Your enterprise assets - proprietary, durable, governed
Data connectorsKnowledge TwinsAgent and task flowsApp integrationsEvals and KPIs

Volatility rains down. It bounces off the layer - and only what passes your evals reaches your assets.

Three scenarios, replayed

Where enterprises lose control - and the AI Intime response.

Tech churn, absorbed

Models swap behind the platform, proven against your tests before cutover. The durable asset is the business solution, not the LLM snapshot.

  1. The LLM sits behind the platform, never inside your application
  2. Expected behavior is captured as business test cases
  3. Replacements are proven against those tests before cutover
  4. Your flows, integrations and business logic stay intact

Capacity, dedicated

Provisioned for your workload only: your region, your data center, your site. Capacity becomes an engineering decision you make, not a lottery you enter.

  1. Dedicated capacity, provisioned for your workload only
  2. You choose placement: your region, your data center, your site
  3. Right-sized LLMs make dedicated headroom affordable
  4. GPU sizing engineered from known volumes, hybrid burst on your terms

Economics, owned

Each task routed to the minimum sufficient LLM; external tokens replaced where justified. Cost per task becomes a decision you own.

  1. Preserve: same platform, connectors, flows, context and evals
  2. Right-size: route each task to the minimum sufficient LLM
  3. Replace: move from external tokens to optimized runtime where justified
  4. Own: make cost per task an engineering decision you own

Sounds too neat? Prove us wrong.

Play around with a synthetic data set, and compare against any AI tool.

Try it yourself
What sovereignty actually means

'Sovereign AI' is a wish for freedom of action.
With AI Intime, that wish is a reality.

The claim from the top of this page, made concrete: four layers where that freedom is real.

Layer 1

Data and knowledge

Layer 1

The moment it bitesAn OEM supplier audit asks exactly where your batch records and formulations travel. 'Into a vendor's shared tenant' ends deals. 'Never past our boundary' ends audits.

Tap to flip back
Layer 2

Applications and agents

Layer 2

The moment it bitesSix months of validated workflows should not hang on one vendor's renewal terms. Portable task logic means you can change platforms without rebuilding the business.

Tap to flip back
Layer 3

LLMs and runtime

Layer 3

The moment it bitesYour EU sites cannot ship data to a US-hosted model, and your US sites will not wait for EU capacity. One platform, different runtimes per jurisdiction - every site compliant, nobody blocked.

Tap to flip back
Layer 4

Infrastructure and economics

Layer 4

The moment it bitesA three-year business case cannot stand on month-to-month cloud pricing. Owned infrastructure turns AI from a variable bill into an asset: budgeted, depreciated and audited on your terms.

Tap to flip back
The approach

Prove first. Then right-size.

Step 1

Start from the business outcome

Step 2

Throw maximum intelligence at it

Step 3

Right-size against your evals

Step 4

Engineer it for production

Let AI reason. Let enterprise systems execute.

The LLM never becomes the system of record. It understands, reasons and routes. Execution happens through governed data, business rules, APIs and audited workflows.

Proof

Deployed where failure is not an option.

A global chemicals leader, where generic agentic platforms hit the wall

A customer engagement team received complex technical documents in multiple languages from sites across three continents. Leading agentic AI platforms could not process them with the predictability the business required: critical context was implicit, and the LLMs guessed. We studied the documents and the users, built a custom parsing and context layer, and had a live proof of concept running in one week. The solution is now scaling globally.

live proof of concept in one week

Air-gapped by design

Some deployments run completely air-gapped: we deliver containerized packages, the customer's own team deploys them, and we have no visibility into what runs inside. That is the standard the architecture is built to.

20+years of manufacturing digital transformation
300+plant deployments
60+countries

Our proof is interactive: a dataset, fourteen queries, your own AI tools against ours. Prove us wrong.

Try it yourself
Use cases

Many use cases. One platform underneath.

Analytical Lab ReportsIn production

Understands batch, sample, test method and parameter relationships. Query precisely by batch, test or parameter value.

Knowledge TwinIn production

Correlates specs, test records and investigations across systems and projects. Natural-language access to engineering knowledge.

Instant reports and dashboards

Maps user intent to KPIs and builds reports, charts and dashboards on demand from your data sources.

Voice-led factory workflows

Hands-free voice access to every agent on the shop floor, with camera capture routed to teammates or tickets.

Intelligent customer operations

Reads customer emails and drafts replies from ERP, CRM and lab data. Human-in-the-loop by design.

OEM and key account support

Any team member queries any account’s knowledge: specs, tickets and trends across batches and complaints.

Two of these run in production today. The rest sit on the same foundation - no second build-out.

One year. Unlimited ambition.

Pick the use cases. We prove the outcomes in your real context, then engineer them for scale - sovereign, in your infrastructure, no lock-in. If strategy changes, you keep what was built.

Book a callProve us wrong - try it yourself

On-prem. Your data never leaves your boundary.