Enterprise AI only works when the data beneath it means the same thing everywhere. We build the canonical semantic layer that makes that possible.
These tools assume alignment exists. We create it — from the first shared definition to the last query in production.
Like a three-legged stool, enterprise data coherence stands only when all three are present. Remove any one leg and the whole system wobbles.
A single, agreed model of what your business concepts are — not a copy in each system, but one authoritative source all systems reference.
The definitions, interpretations, and frames of reference that let Sales, Finance, and Engineering talk about the same Customer without disagreeing.
The mapping layer that connects canonical meaning to every system that produces or consumes data — so the same concept is the same thing everywhere.
We work with the architects, stewards, and leaders responsible for making data trustworthy at scale.
Tired of rebuilding the same canonical model in every new system? We give you a methodology and tooling that makes it build once, reference everywhere.
Responsible for data quality but fighting a losing battle against silos? We create the semantic layer that governs meaning before it reaches the data warehouse.
Your models are only as reliable as the meaning of the data they train on. We ensure that meaning is consistent, documented, and auditable.
Migrating systems, consolidating platforms, or standing up a data mesh? Semantic alignment is the work that makes everything else stick.
Semantic Foundations is an independent AI data readiness consulting practice founded by Laura Hickman. With a career spanning HP, Apple, and Intel, Laura has spent over two decades solving the hardest problem in enterprise data: getting every system to mean the same thing.
The practice combines deep expertise in canonical data modeling, taxonomy and ontology design, semantic alignment, and content pipelines with a methodology built specifically for the demands of AI-ready data foundations.
The Mosaic platform emerging from this work is the productisation of that methodology — making scalable semantic alignment available beyond any single engagement.
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