Your data. Your control. Your private AI.

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. delaware designs and operates the environment, including the AI gateway, and is the single partner accountable from strategy to daily operations. team.blue, active in more than 20 European countries, supplies the dedicated GPU capacity in its European datacenters. imec measures and tunes the models on each customer's own workloads, with its aistack benchmarking 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, air-gapped, with no outbound traffic: 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. EU owned matters as much as EU located: a European region of a US provider stays within reach of the US CLOUD Act.

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. Mistral is available for organisations that prefer a European model for their most sensitive work.

Governance is becoming a legal duty, and the gateway is where you prove it. 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, whether the question comes from the AI Act, NIS2 or DORA.

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.

With an assessment of two to three weeks, based on your own data: where AI runs today, where you are exposed and who sees your data. The gateway starts in observe mode in front of your current providers, with first traffic in two weeks and production in six to eight. No GPU is needed to start; private capacity is added when your usage justifies it.