Decision criterion
Your AI projects don't fail because of the model. They fail because of the data.
Most AI initiatives run into the same reality: non-historized data that nobody can trace or trust. No model, however powerful, compensates for the lack of a foundation. A Data Vault provides exactly what's missing — full history, traceability and business semantics.
- Complete history, without overwriting past states
- Verifiable lineage from source field to training dataset
- Quality Measured and Attached to Each Record
- Explicit business semantics, usable by agents
The Four Requirements
What an AI use case really requires
Ask your data scientists: the same four shortcomings arise in every delayed project.
Historical depth
A predictive model needs the state of the data at the moment of a past decision, not its current state. The Data Vault historizes every change by design.
Traceability
Whether for a regulator or an internal audit, you need to be able to explain what data a model was trained on. Full lineage makes this answer immediate.
Measured reliability
Training on data of unknown quality industrializes bias. The beVault quality score travels with every dataset served.
Business meaning
Business keys, relationships, and shared definitions provide agents with actionable context: they handle concepts, not cryptic column names.
Trajectory
From the warehouse to AI, without detours
Our clients don't launch an “AI project”: they make their foundation usable, and use cases then become achievable one after another.
- 01
Consolidate the foundation
Critical sources are integrated into a Data Vault, historized, and qualified. This is the only truly structural step.
- 02
Cleanly expose
Marts, APIs and the MCP Server give data science teams and agents governed access, with the same permissions as the rest of the organization.
- 03
Industrialising use cases
Reproducible, versioned and documented training sets: a model can be retrained identically six months later.
- 04
Connect your agents to the platform
Via the API and the MCP Server, your agents can query the metamodel, trigger loads and read lineage — with the same access controls as the rest of the organization.
Key takeaway
AI-Native does not mean "adding a chatbot"
A truly AI-ready platform exposes its metamodel, lineage and quality indicators to automated systems, under access control. It's this openness — API, scoped keys, MCP Server — that makes agents useful and governable. Everything else is just a conversational interface bolted onto data nobody can explain.
Next step