VaultSpeed automates the hardest layer of the data stack: integrating raw, fragmented data from dozens of source systems into a governed data foundation, and turning that foundation into trusted, AI-ready data products.

The platform operates exclusively on metadata. No customer data is extracted, moved, or exposed. This makes it a natural fit for cloud-native environments and equally for sovereign, on-premises deployments with strict data residency constraints.

From metadata, VaultSpeed generates native, deterministic transformation code for all major cloud and on-premises data platforms. Code generation is rule-based: no LLM in the code path, no hallucinations by design. No proprietary runtime, no lock-in. You own everything it produces. The platform integrates with your existing modelling, cataloguing, and governance tools, not replaces them.

Our agentic framework extends the platform to work on any data vault, yours included. Built with VaultSpeed, dbt packages, or by hand, the framework brings a governed business model, full lineage, a semantic layer, and data products with contracts. AI agents handle source analysis, modelling, semantic layer design, and data product specification. Your team reviews and validates instead of building from scratch.

At the centre sits an enterprise context store that captures every business definition, mapping decision, and source conflict resolution across the entire data estate. It gives AI agents the structured context they need to reason correctly, not guess from column names. Fewer tokens, fewer hallucinated joins, provably cheaper to run.

The store compounds. Every source onboarded, every definition approved, every domain modelled makes the next one faster and smarter. What took a fifty-person centre of excellence and a decade of institutional learning is now accessible to a ten-person data team.

VaultSpeed

This is a session for data architects and engineers who are being asked to make architectural decisions in real time as AI tooling proliferates. It takes a position: the current conversation about AI in data engineering overweights the intelligence layer and underweights the foundation it depends on. Better foundations mean better AI, lower inference cost, and stronger governance, and that foundation is a governed context store, models, lineage, and transformation logic held in one place, not scattered across scripts and someone's memory.

Every practitioner already knows context quality determines output quality — this session gives you the vocabulary and trade-offs to argue it with the AI-tooling decisions landing on your desk right now.
