Your AI. Your data. Your control.

One governed entry point for every AI request in your company. Copilots, agents, developer tools and business apps all talk to a single endpoint, compatible with the OpenAI and Anthropic APIs, so existing tools connect without a rewrite. Per request, the gateway decides which model may handle it, applies your policy and logs who did what.

Three partners, one accountable offering. Combell, part of team.blue, provides the dedicated GPU servers in its European datacenters. delaware designs and operates the environment, including the AI gateways, and is the single partner accountable from strategy to daily operations. imec tests and tunes the models on each customer's own use cases, with its aistack environment, so that model selection becomes an engineering decision instead of a gamble. Together that gives you capacity that is European in ownership, certification and management.

You cannot ban shadow AI. Almost half of the people using generative AI at work still do it through personal accounts, outside every policy and audit trail. What works is making the sanctioned path the easier one: good models, one login, no friction. Then usage moves onto the gateway by itself.

Not for anything that matters. Your private AI runs on dedicated capacity in European-owned data centres and models, prompts and logs stay inside your perimeter. Frontier models are only used for non-sensitive work, only when your policy allows it, and personal data is filtered out first.

For most work, yes. On coding, the best open-weight models now sit within a few points of the frontier. For the hardest reasoning, frontier models still lead. That is exactly why the gateway routes per task instead of standardising on one model, and why you can switch centrally the moment a better model appears.

Model weights are numbers, not software: a model file cannot run code, open a connection or report back. On air-gapped capacity there is simply no way out. What remains is trained behaviour, such as bias in how a model answers, so every model is tested before it goes live and the gateway applies your policy on top.

Governance is becoming a legal duty, and the gateway helps organizations document compliance with the AI Act, NIS2 and DORA. It keeps a log of every AI request, enforces a policy per model and use case, and gives evidence of where your data was processed. That is the material your auditor asks for.

No. The gateway sits in front of the providers you already use, so teams keep their tools while you gain the view and the control. It is an extra lane next to your Microsoft landscape, not a replacement, and if you run fully on Azure, the same governance layer runs on Azure API Management.

On the contrary. Moving towards your own environment provides cost transparency as you're not dependent on changing token-based pricing models. You lease the infrastructure and we make sure that you get the most tokens out of that infrastructure. In our first deployments, we saw cost savings of up to 50%

With the online assessment tool, working from your own figures: it shows today's AI cost, a 24-month projection, the possible savings and the point at which owning capacity pays off. The gateway can then stand in front of your existing AI providers and sovereign capacity is added once your volume justifies it.