AI that gets past the demo and into production, governed for the EU AI Act.

Use-case discovery and feasibility

Ideas are easy to list. The hard part is spotting the two or three that will actually pay back once they meet your data and your budget. A use case that looks clean on a whiteboard often turns on an SAP master-data change nobody scoped.

• A shortlist of use cases ranked by feasibility and payback, scored against the data and systems you actually run.
• Feasibility judged by people who have taken six-figure Azure AI engagements through presales in manufacturing and chemicals.
• Use-case identification inside the Microsoft and enterprise systems you already run, not a greenfield wishlist.

Generative AI on Azure: document processing, search, and Copilot Studio

A search demo over ten documents impresses a room. Point the same setup at 500 users and a real document store, and it starts returning answers that are wrong, or that someone was never allowed to see.

• Document intelligence that turns purchase-order PDFs into validated SAP orders, not just extracted text.
• We have run enterprise search and RAG (retrieval that answers from your own documents) for 500-plus users, grounded and access-scoped to who is allowed to see what.
• Copilot Studio and Azure AI Foundry implementations built on your data with the retrieval evaluated, not assumed.

Agentic systems and multi-agent orchestration

One agent calling one tool is easy. The moment several agents work together, they have to hold state and fail safely when something goes wrong, and most teams do not design for that before they build.

• Agentic systems on the Microsoft Agent Framework with MCP (the standard that lets an agent call your tools) and A2A (agents calling each other), including custom MCP servers for your own tools and data.
• Multi-agent orchestration across Claude, GPT, and Gemini, routed to the model that fits each step rather than one vendor by default.
• Guardrails, state, and failure handling designed in, so an agent that goes wrong stops rather than improvises.

Machine learning and predictive analytics

A model that scores well in a notebook has proved nothing in production. What counts is the downtime it prevents or the price it gets right, on data that keeps changing.

• Industrial computer vision and anomaly detection running against live process data.
• Predictive models for downtime and pricing, built and validated on your historical data.
• A model taken past the proof of concept to something operations can rely on.
• Every prediction ships with the validation record attached, so a downtime call or a price recommendation can be traced back to the data it was trained on.

AI governance and EU AI Act readiness

EU AI Act obligations attach to the system before it ships, not after. Retrofitting Annex IV documentation and a risk classification onto a system already in production costs far more than designing them in.

• Each AI system mapped to its EU AI Act risk category before design, with the obligations that category carries built into delivery.
• Annex IV technical documentation produced as an engineering deliverable, not a policy template handed over at the end.
• An AI governance practice shaped by a practitioner who has built and led an AI Centre of Excellence.
• The same practitioner holds the AB-731 AI Transformation Leader certification, carried into every engagement rather than handed to a subcontractor.

MLOps, AIOps, and operations

The cost of an AI system shows up after launch. A model drifts as the data changes, and with no one watching, the drift goes unnoticed until a prediction is wrong at the worst moment.

• AIOps with predictive analytics and anomaly detection watching the systems that watch your estate.
• A path from proof of concept to production, with monitoring, retraining, and rollback defined before go-live.
• An operations retainer, so the model stays governed and accurate after handover.