Talk to me directly, no obligation.
Move useful AI from an idea into daily work.
Turning AI hype into a scoped, governed roadmap: use case, data, model choice, cost, in that exact order.
The problem
Most AI pilots fail because the model gets picked before the use case. Readiness work upfront saves the wasted quarter that follows.
An AI Foundry pilot worked technically, but nobody could say what success meant. We define the metric before the next pilot starts.
Copilot rolled out on top of years of accumulated oversharing. We assess and fix the access model first.
Many pilots get stuck here: live, but nobody knows if access is actually safe.
And here: the demo impresses, but nobody can say what success means.
Make enterprise knowledge securely usable with AI: unlock SharePoint, documents and enterprise data sources through RAG and Azure AI Search, with permission-aware retrieval, source citations and answer evaluation.
AI that doesn't just answer but supports or executes defined processes: workflow agents, service desk assistants, document processing and system integrations with human-in-the-loop. As a specialist engagement: agentic software engineering for development teams.
Secure Azure AI foundations for production workloads: Azure AI Foundry, identity, private endpoints, networking, data residency, governance, evaluation and cost control. Also relevant when existing Copilot or AI solutions hit cost, scale or governance limits.
Goal, purpose, usage and expectations are defined before any technology conversation.
Availability, quality and access rights of the required data are evaluated.
Model and platform, for example Azure AI Foundry or Copilot, selected to fit the use case.
Clearly defined metrics agreed before the first prompt is written.
Owner, cost model and monitoring for production operation.
Typical engagements
In 30 minutes, we'll clarify together:
Prefer to write? hello@clouddream.team
30 minutes · Microsoft Teams · No obligation
