AI-Ready Data. Governed, Semantic, and Clean.
AI models are only as good as the data they learn from. Datavault Builder delivers a structured, governed data foundation with automatic lineage, business-aligned semantics, and clean historical records — ready for LLMs, ML pipelines, and AI-powered analytics.
- 100% Automatic data lineage — column level, always up to date
- 14.7 min Average time from requirement to production
- 400% Productivity increase over the full project lifecycle
What makes data AI-ready?
Clean data is not enough. AI needs semantic structure, full lineage, and governance — built into the architecture from day one.
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Automatic Data Lineage
Full column-level lineage from every source system to every AI/BI consumer — generated automatically. Never manually maintained. Always accurate for model governance and explainability.
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Business Semantics Built In
Data Vault 2.0 models real-world business entities as Hubs and Links — the semantic structure your AI models need. No post-hoc annotation. The meaning is in the architecture.
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Governed at the Source
Ownership, retention policies, and data quality rules enforced at the raw vault layer — not retrofitted later. Every AI input is traceable back to a governed, auditable origin.
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Clean, Historised Data
Every data change is tracked and historised automatically. Point-in-time snapshots ensure your training data reflects exactly what was true at any moment in history.
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Semantic Metadata for Every Entity
Every hub, link, and satellite is self-documenting. Descriptions, owners, and lineage context are available for LLM retrieval, data catalogs, and governance tools.
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AI & ML Platform Delivery
Push governed, clean data directly to Snowflake, Databricks, BigQuery, or any platform where your AI pipelines run. One automated pipeline — no manual export.
From raw source to AI-ready mart — automated
Most teams building AI products spend 60–80% of their time cleaning and preparing data before any model training begins. Datavault Builder automates this pipeline:
- Raw Vault — every source integrated with full historisation and lineage
- Business Vault — business rules and computed attributes applied once, reused everywhere
- Mart Layer — clean, semantically aligned datasets delivered to your AI platform
- Automatic lineage — every mart field traces back to its source, column by column
The result: data your AI teams can trust — with the governance your compliance team requires.
Trusted by data-driven teams across industries
Frequently Asked Questions
- AI-ready data has four properties: it is clean (no duplicates, no silent quality failures), historised (time-stamped with full change history for accurate training), governed (every field has an owner, lineage, and agreed definition), and semantic (the structure reflects real business entities, not just raw technical tables). Data Vault 2.0 provides all four by design.
- Large language models and retrieval-augmented generation systems need structured, well-described data. Data Vault Hubs represent business entities (Customer, Product, Contract) that map naturally to knowledge graph nodes. Automatic documentation and lineage metadata can be fed directly into LLM context windows or data catalog tools used for RAG retrieval.
- Yes. Datavault Builder generates native SQL for Snowflake, Databricks, BigQuery, Azure, and all other supported platforms. Governed marts can be delivered directly to the environment where your ML pipelines and AI models run — no manual export or transformation step required.
See AI-ready data delivery live
20 minutes. We'll show you the pipeline from source to governed, semantic mart — ready for your AI use case.