NeuroCluster AI Gateway
Choose the AI. Control the data flow.
Give your applications and agents a controlled route to approved AI models. Apply configured policies to who can use each model, which information may be sent, and which destinations are permitted.
Model access & policy
Make your AI policy part of the request.
Different tasks require different models. Different data requires different handling. AI Gateway brings those decisions into a shared access layer, so teams can use approved AI services within the boundaries your organisation sets.
Establish approved routes for participating applications and agents. Configure the supported access and enforcement policies for the data and destinations in scope, then validate how those policies behave against representative requests.
Control model access
Define approved models and providers for participating users, applications and workspaces. Keep the model choices aligned with the task and its information requirements.
Apply configured policy
Identify supported sensitive-content patterns and block requests when the configured checks require it. Agree the checks and review rules during implementation, then validate them with representative requests.
Choose permitted destinations
Establish approved private-model or external-provider routes for the applications in scope. Confirm the permitted destinations and handling of fallback routes as part of the deployment.
Understand usage
Review consumption, costs and policy decisions for traffic routed through the gateway. Use that evidence to inform access decisions and operational reviews.
Support oversight
Agree which request and policy evidence your review process requires, together with appropriate access and retention rules.
Illustrative workflow
The right boundary for each request.
A team asks AI to summarise an internal document. A configured enforcement policy checks the permitted route for that request. It can proceed to an approved destination or be stopped for review where the supported policy requires it.
01
Information in scope
The application sends a request through the gateway using an approved route for the internal document.
02
Configured enforcement
Supported checks and enforcement rules decide whether the request is permitted or stopped for review.
03
Evidence for review
Responsible teams inspect usage and policy decisions under the agreed access and retention rules.
Illustrative configured policy. Checks, routing and enforcement depend on the configured controls, supported policies and the data types in scope.
Scope & operating boundary
Start with the traffic you need to control.
Identify the applications, models and data categories in scope. Establish approved routes and policies, then test them against representative work. Connect AI Gateway to participating applications or use it alongside Agentic Cloud and Compose.
Gateway controls apply to traffic routed through the managed route. Direct access to AI services and other routes need their own controls. Agree how request content, attachments, logs and fallback paths are handled in your deployment.
These technical controls support your AI governance programme and can contribute evidence to an EU AI Act assessment. Compliance also depends on the specific use case, your role, organisational measures and the complete system.
European Commission: the AI Act framework- Participating applications and agents
- Approved models and destinations
- Configured enforcement and review rules
- Agreed evidence and retention requirements
A practical first step
Turn policy into an agreed route.
Begin with a defined set of requests. Align the controls with your information requirements and review the results before expanding the scope.
01
Map the AI flows
Identify the applications, users, models and information categories that need a controlled route.
02
Configure the policy
Agree approved destinations, supported checks, enforcement mode and the response to exceptions.
03
Validate the boundary
Test representative requests and review evidence, retention and fallback behaviour with the responsible teams.
How it connects
One enterprise foundation behind the work.
Use the product capabilities that fit the workflow. Shared business context, identity, permissions and connections provide the foundation; people retain ownership of consequential decisions.
Cloud & Pods
Compare managed cloud training and extra capacity with a private deployment for local development and protected data.
Agentic Cloud
Apply the approved model route to participating agents working with shared enterprise context.
Compose AI Studio
Bring controlled model access into scoped applications and reporting workflows.
AI Transformation
Establish responsibilities and practical operating rules around AI use.
Plan with NeuroCluster AI Gateway
Start with the work that matters.
Tell us the process goal, the information involved and the environment you need. We will review the supported capabilities and agree a practical first scope.