GenAI without a foundation is theatre.
Every team wants a copilot. Without a governed semantic model, the answers will be wrong, or worse, plausible and wrong.
Every team wants a copilot, no governed semantic layer underneath, embeddings sprawl across pilots. Fix it with a Microsoft Fabric semantic model + Purview-governed feature & vector store + Azure AI Foundry MLOps. Foundation live in 12 weeks, 3 copilots shipped on top.
If half of these sound familiar, this is your fight.
No formal assessment needed, just an honest look at the daily friction.
You're piloting GenAI tools without a clear data layer to ground them.
There's no central semantic model, so every AI use case rebuilds context from scratch.
Vector embeddings live in three pilot environments and nowhere governed.
You can't answer "what data is this AI grounded on?" within five minutes.
Sensitive data leaks into AI prompts because there's no DLP at the data layer.
AI Act-relevant context (lineage, classifications, owners) doesn't exist for the data feeding models.
Why it happens
No governed semantic layer
Without a Fabric semantic model + Purview governance, AI grounds on whatever data team has access to, which is usually too much or too little.
No feature store
Each AI use case rebuilds features from scratch, multiplying effort and divergence.
No vector strategy
Embeddings sprawled across pilots: no shared embedding store, no governance, no reuse.
What it costs you
Hallucinated decisions
AI grounded on the wrong data answers confidently and incorrectly, until the consequence hits the customer.
Stalled use cases
Without a foundation, every new AI initiative starts at week one. Most never reach week ten.
AI Act exposure
High-risk models without documented training/grounding data are unauditable, which means unshippable.
The shortest path from problem to results.
Microsoft-first stack. Belgian and Estonian engineering. Senior team kickoff through delivery to support.
Microsoft Fabric semantic model
A governed, certified semantic layer with row-level security: the spine that grounds every AI use case.
See solutionPurview-governed feature & vector store
Reusable features and embeddings with classifications, lineage and access policies: AI-ready and audit-ready.
See solutionAI architecture & MLOps
Azure AI Foundry, Semantic Kernel, evaluation harnesses, so AI use cases ship to production with confidence.
See solutionWhen the foundation came first, and the copilots followed.
Fabric semantic layer + Purview · AI-ready in 12 weeks
A Belgian public-sector organisation with five AI pilots and no governed data layer. We built a Microsoft Fabric semantic model, governed it with Purview, established a shared vector store and feature catalogue. Three GenAI copilots shipped to production in the next quarter: all grounded, evaluated and AI Act-tiered.
Build the foundation before the copilot.
Free 60-minute readiness call: we'll map your AI foundation gaps.
Common questions, direct answers.
Why does AI need a "data foundation", can't we just connect ChatGPT to our data?
You can. The result will be wrong, often. Models without governed grounding hallucinate confidently. The foundation is what makes "this answer is reliable" a true statement, not a wish.
What goes in an "AI-ready" semantic layer?
A Power BI semantic model with row-level security and certified definitions; a feature store for reusable ML features; a vector store with classifications, lineage and access policies. All three on Microsoft Fabric.
Do we need a separate vector database?
Fabric now has a Vector Search preview. Plus Azure AI Search integrates natively. Dedicated vector DBs (Pinecone, Weaviate) only earn their keep at very high scale (>100M vectors).
How long until foundation pays back?
Twelve weeks to live foundation. The payback is multiplicative. Every new AI use case after that starts at week three not week one. Customers typically ship 3-5 copilots in the next quarter.
What about Microsoft Copilot, does that count as AI-ready?
Copilot for M365 grounds on your M365 graph (good for office tasks). Copilot Studio + AI Foundry let you ground on YOUR data with YOUR rules, which is what enterprise AI needs.