Why CoreModels

Your systems already know what your data is. None of them knows what it means across the others. CoreModels holds each model as it is and keeps the mappings between them alive.

A single-source annotation tool answers one question about a field: what does it mean? An agent working across the enterprise needs four answered — what it means, where it lives, what it connects to, and how it sits in the model. Answering all four across sources is the product, and the reason the stack you already run is not enough.

The gap, in four dimensions

Dimension
Without a core model
With CoreModels
Meaning
A field’s definition lives in a YAML comment, a wiki page, or one person’s memory. Two tests that disagree both pass.
Definitions, permitted values and the mistakes an agent keeps making sit on the element itself, where a machine can read them.
Location
The same entity exists in the warehouse, the CRM and the content model, and nothing says which one the business considers true.
Every entity is located: which system holds which sense, and which sense is in force at this seam.
Connection
The join across systems is re-derived by every pipeline and guessed by every agent, differently.
Joins and equivalences are mapped once — customer_id to accountRef to Person — and read by every consumer.
Relationship
How a term sits in the model — parent, subtype, mapped equivalent to a public standard — is written nowhere.
Relationships are typed, versioned and traceable, from your own schema to the standard it conforms to.

What your stack already does, and what it does not

A transformation tool knows its project. A catalog knows its tables. A metrics layer holds measures; a master-data system holds identity; a component content system holds components; a knowledge graph holds whatever someone loaded into it. Each is real, and each is expert in something the others cannot hold. Pointing an agent at any one of them is a reasonable first move, and for questions that stay inside that system it works.

None of them holds the mappings between the rooms. None can say that customer_id in the warehouse is the same entity as accountRef in the CRM and Person in the content model — or which of the three the business considers true at this seam, or who is allowed to change that. That judgment does not exist in any of your systems. It exists in your people, and until now it has stayed there.

Why CoreModels

Bridging the gap between humans, systems and AI.

Built for modern data pipelines

At the heart of ETL and ELT workflows, CoreModels functions as a structural and semantic control layer that governs the transformation process across data systems. It manages how data is reshaped, validated and mapped, ensuring every transformation maintains consistency with both technical definitions and business meaning. By centralizing transformation logic, mappings and schema relationships, CoreModels lets pipelines adapt as models evolve.

Alignment across humans, systems and AI

When schemas and transformation logic are hidden in code, subject-matter experts rely on developers for every update, and AI systems lose context. CoreModels exposes and connects these definitions, establishing a shared foundation where humans and machines interpret data consistently. That alignment minimizes friction, eliminates redundancy, and reduces rework across complex ecosystems.

Designed for flexibility

Data structures, APIs and AI workflows evolve faster than traditional tools keep up. CoreModels is a graph-based low-code modeling platform built on adjustable meta-model templates that support a wide range of representation formats. Whether managing JSON Schemas, JSON-LD definitions, or the configurations that drive transformations and orchestration, it adjusts to organizational and technical needs.

A unified environment for data ecosystems

Teams model, extend and connect structural and semantic definitions within one environment, which keeps them resilient as data ecosystems change. The unified approach minimizes duplication, prevents schema drift, and ensures reliable, governed data flow across distributed and hybrid architectures.

Neither humans nor AI is a dependency

The governance-minded buyer worries that agents will decide what the data means. The automation-minded buyer worries that every request will wait on a person. CoreModels is built so that neither is true. The machine proposes first: it infers the joins, the equivalences and the relationships it can see across every source. Your human team settles the calls that carry consequence — not every field needs a ruling; the ones that do, get one — and commits them in a shared workspace, with AI as collaborator. Nobody hand-labels a warehouse. Nobody ships a definition the business never agreed to. And once settled, the people are out of the request path: the model answers.

How it works, step by step →

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Next in the frameworkHow it works →Connect and ingest, model and map, collaborate and govern, expose and execute, adapt and scale — the mechanism behind the three steps on the landing page.

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