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VaultSpeed

VaultSpeed automates the construction and maintenance of Data Vault 2.0 data warehouses. The platform takes a business model as input and generates production-ready code as output: DDL, loading procedures, transformation logic, and orchestration, across all major cloud and on-premise database platforms including Snowflake, Databricks, Azure Synapse, SQL Server, and Oracle.

Code generation is deterministic and template-based. Same input produces same output every run. Versioned, auditable, deployable through CI/CD.

VaultSpeed sits in the Belgian data software cluster alongside Collibra, Soda, and Luzmo, applying the AI-Native pattern at the data-infrastructure layer rather than the analytics layer.

Agentic Framework (2026)

In early 2026 VaultSpeed extended the platform with an agentic layer built on a graph-based knowledge model of an enterprise's full data landscape.

The knowledge model captures four layers:

  • Business Entities: what the organisation cares about
  • Integration Strategies: how concepts are resolved across sources
  • Physical Structures: the vault implementation
  • Semantic Definitions: what the data means in business terms
AI agents work above and below the vault. Upstream they handle source analysis, business key identification, entity mapping, and conflict detection. Downstream they generate governed semantic layers, metric definitions, and data product structures for tools such as Snowflake Cortex and Databricks Genie.

Agents propose. Humans approve. Production code generation stays deterministic.

The framework operates on metadata only. It never processes actual data. The layer is LLM-agnostic; customers connect their own model provider.

Customers and Markets

Enterprise deployments across financial services, insurance, telecom, manufacturing, and legal/media. ISO 27001 certified. HQ in Leuven; additional offices in London and Boston.

Why It Matters

VaultSpeed compounds customer learnings into a shared platform: every deployment teaches the system new edge cases, source patterns, and integration strategies. That moves Data Vault 2.0 architectures from a discipline requiring the largest, most sophisticated data teams into reach for much smaller organisations. In the context of this wiki, VaultSpeed is a clear example of the B2B SaaS and Enterprise AI patterns applied to data infrastructure.

Positioning

VaultSpeed is relevant for organisations that are building or maintaining a Data Vault 2.0 warehouse, need deterministic and auditable code generation rather than AI-written production code, want a governed semantic layer on top of the vault for AI/BI consumption, or are migrating from a hand-built or dbt-based vault. It is not a general-purpose ETL tool, a data catalog, or a BI platform.

Links

  • Website: https://www.vaultspeed.com
  • LinkedIn: https://linkedin.com/company/vaultspeed
  • Contact: info@vaultspeed.com

Key People

Funding History

In the media3

  • Patrick Van DevenInterviewVaultSpeed5mo ago

    Patrick Van Deven discusses his career in data warehousing, including roles as chief data officer at BNP Paribas and partner at Deloitte, before joining Valipac and Recitata two years ago. He explains that Valipac manages industrial and commercial packaging waste streams across Belgium by treating waste as data, tracking packaging from market entry through recycling globally, and organizing both the physical and financial flows of waste recovery.

  • Patrick Van DevenPodcastSabine VdL3w ago

    Patrick Van Deven discusses how most regulated industries still rely on outdated data infrastructure built decades ago that cannot support AI adoption, making companies that simply layer AI onto existing systems vulnerable to regulatory problems rather than becoming true frontier firms. He argues that automating the deterministic data layer underneath AI agents is essential, and that organizations should treat AI agents like employees with clear briefs and operating parameters rather than expecting legacy data pipelines to magically enable AI-native capabilities.

  • Patrick Van DevenTalkVaultSpeed13d ago

    Patrick Van Deven discusses how AI is transforming data engineering and introduces the webinar's main topic about the limits of AI and the importance of human decision-making in building data pipelines. He and his guest Hans explore core business concepts, natural business relationships, and logical data modeling terminology, emphasizing that these foundational concepts should be based on how businesses actually operate rather than technical abstractions.

Recent mentions8