Use case — AI Foundation
Build the foundation that your AI projects can rely on.
Connecting a model to a database isn't enough. What's almost always missing is what surrounds the data: its history, its business meaning, its metadata, its quality controls and its traceability. Without that, every new initiative starts over from raw, fragmented data.
The situation
Every AI initiative starts from scratch
A first use case is scoped. A team extracts data from three systems, cleans it on its own, produces a usable dataset — then moves to the next one, and starts over. None of that work is reusable.
The problem isn't data access: most organizations can technically read their systems. What's missing is a foundation where history is preserved, business concepts are named, quality is measured, and the origin of every value can be explained. Without this foundation, a model's results remain hard to defend before a committee.
What beVault provides
Context and history built into the foundation
The Data Vault overwrites nothing: every state is kept, dated and linked to its source. That's what makes a dataset reproducible a year later.
- Historique
- Successive states of the data are kept and timestamped. A retrospective analysis relies on the actual state as of the relevant date, not on today's state.
- Business context
- Entities and relationships are named using the organization's own concepts, and documented in the metamodel rather than guessed from column names.
- Metadata
- Definitions, owners and structures are described in metaVault and can be retrieved via the API and the Data Catalog.
- Quality
- Declared rules are applied and measured at every load, and exceptions are visible rather than silent.
- Lignage
- The path of a value, from source to consumed output, remains traceable — a practical precondition for any explanation.
How it works
From foundation to data products
Data isn't exposed raw to consumers. It's published as documented data products, built once and reused across multiple use cases.
- 01
Integrate and historize the sources that matter
The systems selected for the use case are loaded into the Raw Vault, as-is, with their origin and load date.
- 02
Name the business concepts
Customers, contracts, events: entities are modeled and documented in metaVault, giving teams and tools alike a stable vocabulary.
- 03
Publish documented Information Marts
A dataset intended for a specific use — reporting, application, training — is exposed with its definition, scope and lineage.
- 04
Make these products consumable
The marts feed BI tools, applications and, within the approved scope, agents via beVault's API and MCP Server.
Govern outputs too
An AI output is still data: it deserves the same treatment as any other.
Scores, classifications and results produced by a model can be fed back into the vault like any other source: historized, dated, and traced to their origin. You then know exactly which result was produced, when, and from which data — making a review possible.
Access remains governed: what agents can query depends on the access rights and data products exposed, not on direct, open access to the warehouse.
Next step