AI you can take to an auditor, not just a demo.

Use-case discovery and feasibility

The hard part is not finding an AI idea. It is telling the three that pay back from the dozen that will not survive contact with your data, your existing systems, or a budget review. A use case that looks clean on a whiteboard often turns on a change to SAP master data that 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: RAG, Document Intelligence, Copilot Studio

A retrieval demo on ten documents impresses a room. The same pattern on 500 users and a real document estate breaks on grounding, access control, and the answers no one checks.

• Document intelligence that turns purchase-order PDFs into validated SAP orders, not just extracted text.
• Enterprise search and RAG running 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

An agent that calls one tool is a script. An agent that coordinates several, holds state, and fails safely is an architecture, and most do not draw the line between the two before they build.

• Agentic systems on the Microsoft Agent Framework with MCP and A2A, 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 is not a result. The result is the downtime it prevents or the price it gets right, in production, 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 an IAPP AI Governance Professional 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: drift, silent failure, and the retraining no one scheduled. A model with no operating model degrades and no one notices until it matters.

• 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.