NewVaultSpeed 8 is hereRead the announcement

Agentic data modeling

Bring AI to data modeling, and automate the code

VaultSpeed connects source systems, the business model and governance in one metadata graph, so the integration layer is generated from the data model instead of built beside it.

Explore data modeling See the platform
Model the data
Organization: northline-bank ▾

What should we build?

⌁Describe a task to start a new session…claude-sonnet-5↑
⌁Ask the agent anything...claude-sonnet-5↑
Agent-sandbox Sandbox⟳ 2%
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Working folder⑂ northline-dvnorthline-bank/northline-dv
▾ docs
business-model.md12 KB
▾ sources
in_crm.yaml4.1 KB
in_core.yaml3.6 KB
README.md2.3 KB
Source control0 changes
Commit message
Stage allCommit
Pushed model/northline-dv4c1e9a2 · Data Vault proposal, 233 decisions
Generate the code

The data model is where the context lives. Build it well and every use case, data product and hard question, asked by a person or by an agent, gets a better answer.

Who it is for

Where teams start

Program owner

You have a modernization mandate and a date.

A first governed data product within the first working sessions. See how your peers did it.

Customer stories

Platform owner

You are leaving a legacy platform or merging two data landscapes.

Native code for Snowflake, Databricks and dbt, in your cloud, with the integrators you already have.

Technologies

Data model owner

You own the enterprise data model and AI needs it.

Model with an agent and hand engineers a machine readable graph.

Data modeling

Governance owner

Agents are entering your data platform and the regulator has questions.

Every agent proposal is reviewed and versioned. The platform holds metadata, not your data.

Deployment and trust

</>You are the engineer and want the technical detail.Go straight to the Platform

Most data platforms get built twice.

Once as a data model, kept in modeling tools and the governance layer. Once as the code that moves the data, written beside it. The two drift apart from the first sprint onward, and the gap is where the maintenance cost lives.

How we close the gap

SPRINT ONEMODELING TOOLSThe modelPIPELINESThe codeTHE GAPmaintenance cost

Customer story · Retail

Data on time, every day. That's the win.

Ruben Missinne, Data & Analytics Officer, Colruyt Group

Colruyt GroupMore than 50 sources moved to Snowflake in under six months, by moving metadata instead of codeRead the story
Rated 4.8/5 by customers on G2All customer stories

Reviews

Real-world reviews that speak for themselves

4.8/5 customer ratingon G2
5/5

1 year into VaultSpeed

VaultSpeed has given us a strong foundation for enabling AI on top of our data platform. By using Data Vault automation, we can structure and normalize data consistently, which is essential when building knowledge graphs, vectorized indexes, and semantic layers for complex AI queries.
G2 reviewerRaymond L.Chief Engineer
Mid-Market (51-1,000 employees)
5/5

A must tool for Data Vault automation

VaultSpeed takes the heavy lifting out of Data Vault. It's business model-driven, so once a source is modeled, we can onboard it into the Raw Vault in less than a week, something that used to take months of manual coding.
G2 reviewerRadhakrishna Nayak K.Senior Business Intelligence Consultant
Enterprise (10,000+ employees)
5/5

Flexible and intuitive automation tool for building Data Vault

VaultSpeed has significantly streamlined the way we build our enterprise Data Vault. The platform offers an intuitive interface and a well-structured approach to implementing Data Vault methodology, making it easy to follow and adopt.
G2 reviewerLærke Lyhne H.Senior Data Engineer
Enterprise (10,000+ employees)
5/5

VaultSpeed is a key enabler in our data warehouse migration program

We have to deal with very large, complex warehouses, thousands of tables, 5,000+ active users, and decades of legacy. VaultSpeed makes it possible to tackle this in manageable, automated steps.
LGLaurent G.Data Warehouse / Modelling Lead
Enterprise (10,000+ employees)
5/5

VaultSpeed is the next generation solution for Data Vault!

VaultSpeed simplifies the data vault build process with built-in solutioning that ensures the build is done correctly. The parameter-based solution allows for mass changes across the solution.
GBGayle B.Data Warehouse Architect
Mid-Market (51-1,000 employees)
4.5/5

VaultSpeed revolutionizes enterprise data modeling

As a Product Owner, what I appreciate most about VaultSpeed is the balance between standardization and flexibility. The platform enforces best practices of Data Vault 2.0, which gives me confidence that our data warehouse design is sustainable and future-proof.
CJChristoph J.IT Product Owner
Enterprise (1,000+ employees)
How it works

Model with an agent. Generate the code.

Agents propose the data model, the context store keeps it machine readable with its lineage and logic, and the generator writes the code the same way every run. The detail is on the Platform page.

Model with an agent

Agents reason over the business concepts, profile the sources and propose the data model and its mappings, in a session you can read. Every proposal is reviewed before it lands.
Model with an agent

The data model becomes a machine readable graph

The context store holds the data model with its lineage and logic in one graph, versioned in Git and open to any agent over MCP. Engineers query it from the harness they already use.
The data model becomes a machine readable graph

Generate the code

DDL, transformation code and workflows, native to Snowflake, Databricks, dbt and the other targets. Same input, same code, every run, with no language model in this step.
Generate the code
See how the platform works
Metadata, not code

Data engineering that works from a shared metadata graph is robust, maintainable and auditable, because the design and business logic live in metadata, not in code.

Runs on your stack

Native code for your platform, running in your cloud

The generated code goes into your Git and CI/CD process and runs on your platform without VaultSpeed in the loop. VaultSpeed holds metadata, not your data, and is available on the marketplaces you already buy through.

SnowflakeSnowflakeDatabricksDatabricksdbtdbtMicrosoft FabricMicrosoft FabricGoogle BigQueryGoogle BigQueryAmazon RedshiftAmazon Redshift

All technologies and partners  ·  Buy through the Azure or Snowflake marketplace

Where to meet us

Coming up
22Oct
VaultSpeed NEXT New YorkEvent · Flanders House, New York
26Nov
Data Dreamland 2026Conference · Hannover · Gold Sponsor
21Jan
Context engineering for AI, with CollibraWebinar · Online · 4:00 PM CET
All events and recordings →

Talk to us

Start with your data model

Tell us who you are and what you want to discuss. We come back within two working days with a proposed slot, and the first session already runs on your own metadata.