AI answers your decision-makers can actually trust.
What if your business users could ask:
“What drove revenue last quarter?” and truly trust the answer? Today, most AI-generated insights look right, but they aren’t.
Without a semantic layer, LLMs (Large Language Models) guess your business logic because no one has explained to them what “revenue” or “churn” means in your company.
With the right semantic layer and data foundation, they execute instructions correctly — every time.
Federico Erroi
Senior Data Engineering Specialist & AI Expert
+49 89 219 099 021
Book your free 30-minute initial consultation now.
1. AI generates SQL but guesses your business logic
LLMs don’t understand your definitions of revenue, churn, or customers. They infer them from tables and often get it wrong.
The results are wrong joins (links between database tables), double-counted metrics, conflicting definitions.
The output looks right. But it isn’t.
2. Runaway cost
LLMs query your data warehouse without understanding what’s actually needed. They scan too much data, run inefficient joins, and over-fetch by default.
The result are exploding compute costs, slow query performance and wasted resources on irrelevant data
The query runs. The bill grows.
3. Uncontrolled access
LLMs don’t understand access policies or data sensitivity. They can retrieve and expose data they shouldn’t.
The results arenauthorized data exposure, compliance violations and loss of control over sensitive information.
The answer is generated. But at what cost?