Data modelling companies, products & suppliers
Semantic or dimensional modelling of data for analytics and AI consumption. Jobs: semantic layers; metric definitions; governed datasets for ai.
What is Data modelling?
Semantic or dimensional modelling of data for analytics and AI consumption. Jobs: semantic layers; metric definitions; governed datasets for ai.
What problems does it solve?
Every team defines 'revenue' differently, so models and copilots disagree.
Typical business use cases
- Semantic layers
- Metric definitions
- Governed datasets for AI
Important capabilities
- Metrics store
- Contracts
- Versioning
What buyers should evaluate
- Who owns metrics
- Change control
- Whether copilots must use the layer
Risks and governance considerations
A copilot that bypasses the semantic layer.
Procurement checklist
- Ownership
- Versioning
- Enforcement
Relevant AI Trustmark assurance
AI Trustmark independent findings appear only when an assessment or certificate exists. Category membership does not imply verification.
Products and providers
Open a product profile for its catalogue details, or use the provider link for the company profile. Claimed listings appear first, then by name. Payment never changes the order.
By AtScale
Discover how AtScale's semantic layer solution can you help make smarter data-driven decisions at scale and build a self-service data-driven culture. AtScale Semantic Layer is
By C3 AI
C3 AI is an enterprise artificial intelligence software company based nearby in Redwood City, California. C3 Agentic AI Platform is C3 AI's platform, from C3.ai, Inc. Published at
By Cube
Cube is the agentic analytics platform, built on a semantic layer — AI-native business intelligence and embedded analytics with answers your team and your customers can trust. Cube
By data.world
Discover data and metadata in seconds and develop data products and analytics that drive your business. data.world AI Context Engine is data.world's platform, from data.world, Inc.
By dbt Labs
dbt is the analytics engineering standard for SQL transformation; dbt Cloud adds AI-assisted development features. Public marketing also emphasises analytics engineering standard.
By Ellie.ai
Full-stack data modeling powered by AI and MCP Server. Collaborative data product design and governance to create a universal semantic layer for agentic AI. Ellie.ai is Ellie.ai's
By Neo4j
Connect data as it's stored with Neo4j. Perform powerful, complex queries at scale and speed with our graph data platform. Neo4j Graph Data Science is Neo4j's platform, from N
By Siemens
Siemens: A global technology leader driving innovation in industry, infrastructure and mobility through digital transformation. Rapidminer Graph Studio is Siemens's platform, from
By Stardog
Stardog makes your AI intelligent with a knowledge graph-powered semantic layer that unifies enterprise data and activates your existing stack for better AI. Stardog Enterprise Kno
Products
Claimed products appear first. Ranking packs and payment do not change this list.
- AtScale Semantic Layer· AtScale
- C3 Agentic AI Platform· C3 AI
- Cube Cloud· Cube
- data.world AI Context Engine· data.world
- dbt / dbt Cloud AI features· dbt Labs
- dbt Copilot· dbt Labs
- Ellie.ai· Ellie.ai
- Neo4j Graph Data Science· Neo4j
- Rapidminer Graph Studio· Siemens
- Stardog Enterprise Knowledge Graph· Stardog
- Stardog Voicebox· Stardog
Related categories
Relevant procurement and assurance guides
Frequently asked questions
What is Data modelling?
Semantic or dimensional modelling of data for analytics and AI consumption. Jobs: semantic layers; metric definitions; governed datasets for ai.
What should not be listed as Data modelling?
Products whose buyer job is feature stores, data-science notebooks, or BI copilots. Those belong on their own category page so search queries are not split.
Has AI Trustmark independently assessed every Data modelling supplier?
No. A category listing is descriptive. Independent assessment is shown only on company or product pages that carry Trustmark evidence.
What model or provider changes should a buyer insist on being told about?
Material change usually includes a new model family, new region, new subprocessor, new write-capable tool, or a change that affects logging, privacy or human oversight. Those changes should trigger evidence refresh rather than a silent release.
How should buyers verify where AI customer data is processed?
Ask for the named processing locations, cloud regions and any subprocessors that see prompts, files or outputs. A directory listing is not evidence of residency. Independent assessment records the locations that were in scope on the assessment date.
Does a TrustMark on one product cover the rest of the company?
No. Independent assessment is scoped to the named organisation and, where relevant, the named product. Category pages list suppliers as a topic label. They do not imply that every listed company has been assessed.
What incident-handling evidence is useful for AI suppliers?
Buyers should see how AI-specific failures are detected, contained and notified — including unsafe outputs, data leakage and unauthorised agent actions. An incident policy that never mentions models, prompts or tools is incomplete for this class of product.
What security testing evidence should buyers request for an AI product?
Ask what was tested, against which version, whether prompt-injection, data-exfiltration and tenant isolation were in scope, and where failed prompts were stored. A generic ISO certificate or a vendor scanner screenshot is not by itself an AI TrustMark assessment.