Azure
AI Foundation
A standardised platform for models, agents and tools, built entirely as code, with governance via Entra ID and built on Microsoft Foundry.
Artificial intelligence has long been part of the working day: your people use ChatGPT, Copilot and self-built agents because these tools make their work easier. That is good, but it raises questions no single tool can answer: which models it runs on, who is allowed to invoke which agent, and where the data ends up. What it takes is not another AI tool but a shared foundation on which all of this can be settled centrally and traceably. That is exactly what the AI Foundation delivers: a standardised base that we build once and that applies to every team.
know or suspect unsanctioned GenAI use inside their own organisation.
AI tools the average company uses. IT knows about four or five of them.
will experience a shadow-AI security or compliance incident by 2030.
What makes the Azure AI Foundation
You want to put AI to work in your own stack, but where to start?
Whether you're building your first agents, stuck with a proof of concept, or have several teams experimenting in parallel, these questions will be familiar:
A model is not an agent. What is missing in between?
Between a language model and something that reliably captures orders or escalates service cases lies a lot of unglamorous work: which model, which tools, which data sources, which guardrails, and what happens when the agent gets it wrong. That work has to happen traceably and repeatably. That is exactly what the Azure AI Foundation is for.
What is Microsoft Foundry and do we need it?
Microsoft Foundry (formerly Azure AI Foundry) is the platform where agents are built: model catalogue, agent orchestration, connections to tools and data sources, evaluation and observability in one place. We build the Azure AI Foundation on top of it and deliver it as a ready-to-run environment, instead of leaving you alone with an empty portal.
Which models can we use, and are we locked in?
You are not locked in. The Foundry model catalogue holds over 11,000 models behind a single API: GPT, Claude, Mistral, Llama, DeepSeek and Microsoft's own model families, alongside curated open-source options. The Model Router goes one step further and picks the right model per request automatically, weighing quality, cost and latency, so simple prompts run on cheap models and hard ones on capable ones. You can swap or add models without rewriting your agents, and whichever model runs underneath, the AI Foundation applies the same identity, guardrails and cost controls.
How do we stay in control of what an agent does?
Through identity and guardrails. Every agent reaches data and workloads through the same Entra identities as the rest of your landscape, runs against role-based access, content filters and network boundaries that are not up for negotiation, and leaves a trail you can audit.
How does the agent reach our data and workloads?
The Azure AI Foundation is the third building block of a shared platform. The Azure Data Foundation gives the agent access to the data that describes the business, the Azure Container Foundation gives its workloads a controlled place to run. All three draw on the same identity and the same guardrails.
And when can we get started?
Your production environment will be ready in 3 to 4 weeks. One workshop for the configuration decisions, deployment from GitHub into your tenant, then hands-on training and handover. Those who want can book the Managed Service right away, and we take care of the lifecycle long-term.
The Holy Trinity
Contact us now
It starts with a Discovery Workshop: half a day on your use cases, the right tier and your next step. We work through the concrete questions with you, which models and agents, which data and guardrails, how operations and costs work, and outline the path to a production AI Foundation. Tell us what you are working on.