Mission
To make it possible for serious organisations to run AI that acts — on their own data, under their own policies, on infrastructure they control, with evidence of what it did.
Every word in that sentence is load-bearing. Acts, rather than answers, because the governance problem only becomes acute when systems change state. Their own policies, because a provider's defaults are not a governance model. Infrastructure they control, because a commitment about where inference happens is only as good as the capacity behind it. Evidence, because in the environments we build for, an explanation that cannot be produced on demand is not an explanation.
Theshiftwearebuildingfor
Each era solved the previous era's problem and created a harder one. The third is where enterprise AI is now.
Whytheexistingstackdoesnotcoverit
Enterprise systems have two well-established models for an actor. A human has an identity, a role, a manager and a review cycle. An application has a fixed set of behaviours that were reviewed once and change through a release process. Decades of security, audit and governance practice rest on those two models.
An agent is neither. It has the reach of an integration and the improvisation of a person. It arrives with no identity of its own, frequently no owner, and a set of permissions inherited from whatever credential was convenient. There is no established practice for reviewing something that decides what to do at runtime.
That gap is what NeuroCluster builds into. Not a better model, and not a faster way to build agents — a layer that decides what intelligence may do, enforces it, and records it.
Whatwebuild
The control plane
Identity, ontology resolution, policy, execution, evidence, evaluation and lifecycle for every agent and model.
The ontology layer
The semantic model that makes governance expressible and grounding possible at the same time.
The runtime
Isolated, observable execution on conformant Kubernetes, in any deployment posture.
Controlled infrastructure
Private AI cloud today, with a longer-term position in purpose-built capacity.
Research
Reasoning, evaluation and language work in Labs, feeding platform capability.
Delivery
The engineering and sector work that gets an organisation to a governed production deployment.
Whoitisfor
Organisations that share a constraint profile rather than an industry.
Asset-intensive operators
Grid, water, telecom, transport and heavy industry, where the consequence of a decision is physical.
Regulated decision-makers
Financial infrastructure, insurance and public bodies, where the decision trail matters as much as the decision.
Organisations with a sovereignty requirement
Where placement, key custody, operator access and exit are contractual rather than aspirational.
Organisations already past the pilot
Where several teams have shipped something and nobody can answer a governance question about the whole.
Whereitisbuilt
The company is built in Europe, in and around the Brainport region, with a research programme connected to European academic and compute ecosystems. That has practical consequences: the engineering assumes European regulatory conditions as the default rather than an export requirement, and the deployment postures reflect what European operators of critical infrastructure actually need.
It is a starting position rather than a category. A control plane for enterprise intelligence is useful to a grid operator in any jurisdiction, and geography is not a value proposition.
Howresearchreachestheproduct
Labs is not a parallel business. It has one output: capability the platform can use.
Bring us one operational problem.
You do not need a finished brief. Bring the problem — we will work out the next step together.
Or book a call with the team