Solutions — For SMEs
A data foundationthat grows with your business.
SMEs often need to structure their data with multiple sources, small teams and rapidly evolving needs. beVault helps build a reliable foundation, improve Data Quality and prepare for analytical and AI use cases without losing control of the environment.
The starting point
When data grows faster than the organisation
An SME rarely accumulates its data problems all at once. They appear as tools are added, dashboards are built in a hurry, and definitions slowly diverge from one team to another.
The tipping point almost always occurs in the same place: a figure is challenged in a meeting, no one knows how to reconstruct how it was produced, and the only person who knows the chain is on leave.
Data ends up spread across several tools — ERP, CRM, invoicing, business files — and indicators are rebuilt by hand at every deadline. A customer, a contract or an order is not counted the same way from one team to the next, and operational systems overwrite past states: reconstructing a situation at a given date becomes difficult.
Quality, for its part, is discovered too late — in the final report rather than at the start of the chain. Knowledge of the processing stays in the heads of a few people and is rarely documented. And the first AI projects run into that same base: data that is neither historised, nor controlled, nor documented.
What beVault provides
A platform to structure, control and evolve your data
Four capabilities that complement each other, driven by human choices that the platform makes explicit and reusable.
Structurer
Model the business meaning and build a Data Vault foundation from metadata.
Intégrer
Connect your sources, map the data and historise changes over time.
Contrôler
Detect anomalies, make them actionable and allow managers to correct the data at its source.
Distribute
Produce documented Information Marts for your BI, analytical and AI use cases.
Starting points
Where an SME can start
An SME can start from a concrete need — reporting, quality, integration or AI readiness — then gradually extend its data foundation. These use cases are not reserved for SMEs: they are possible entry points.
AI-ready Data Foundation
Historise, control and document before opening up AI use cases. See the use case
Data Warehouse Modernization
Build on an existing base without starting from scratch. See the use case
Migration ERP & systèmes historiques
Preserve history and business meaning during a system change. See the use case
BI & Reporting
Indicators aligned with shared definitions and reconstructable over time. See the use case
MDM & Data Integration
Reconcile reference data scattered across several tools. See the use case
Data Quality & Governance
Declare the expected rules and make deviations visible and actionable. See the use case
Trajectory
Start with a clear scope, then progress in stages
An SME does not need to cover its entire information system from the start. A well-chosen domain is enough to establish the method and then expand it.
- 01
Choose a first subject
A priority domain or use case: the one where data is most disputed, or the one blocking a decision or a concrete use.
- 02
Build the foundation
Map the sources and needs, model business meaning in metaVault, generate the structures and load data in a historised way.
- 03
Control and deliver
Declare quality rules, measure deviations, make anomalies visible and actionable, then produce the first Information Marts.
- 04
Extend step by step
Add other sources and domains, evolve the model and open up new analytical and AI use cases, without rebuilding what already exists.
Terms
Conditions adapted to SMEs
Organisations that meet the European SME criteria can benefit from conditions adapted to their size, their scope and their deployment journey.
These conditions may cover the platform, the support services and the way you get started. They are discussed case by case, based on your actual context.
Your teams
Giving more capacity to your teams
A small team can maintain a data platform, provided they do not rewrite the same thing for each source.
On the platform side: less repetitive work, centralised rules and metadata, a more readable model, easier evolution of sources and usable documentation derived from the model.
On the dFakto side: scoping, architecture, implementation, training, skills transfer, support and evolution. These services are optional and sized according to what your teams want to handle in-house.
beVault does not replace a data team: it gives them back time and makes their work transferable.
Environment
In the environment that fits your organisation
beVault is deployed where your organisation can exploit it: on-premises, in your cloud, in PaaS or in hybrid mode. You maintain control of the infrastructure and access, and deployment relies on Docker.
AI use cases
Prepare data usable by AI
beVault does not provide an AI model and does not automatically produce AI results. It prepares the material your AI use cases need: structured, controlled, historised and documented data.
Successive states are kept and dated, so an analysis can be reconstructed as at its date. Declared rules are evaluated at each load, so quality remains measurable. Entities carry the organisation's own names and are documented in the metamodel, so business context is not lost.
Outputs take the form of Information Marts defined for a specific use, rather than raw access to the warehouse. What an agent can query depends on the products exposed, the permissions and the configuration chosen — so AI use cases open up progressively, as the foundation consolidates.
Support
Support tailored to your trajectory
Some organisations progress alone after a scoping phase, others prefer continuous support. dFakto services are optional and scale to actual needs.
Strategy and architecture, implementation, training and skills transfer, long-term support, model evolution and integration of new sources: each strand can be used on its own, whenever it becomes useful.
Business cases
Documented journeys
These cases describe different contexts, with one thing in common: a foundation built step by step rather than in a single project. They are not presented as SME references.
Benchmark
Who is considered an SME?
In European Union law, an SME has fewer than 250 people and respects the ceiling of 50 million euros in turnover or 43 million euros in total balance sheet. Partner or linked companies may also need to be taken into account in the calculation.
Microentreprise
Fewer than 10 employees and up to €2m.
Petite entreprise
Fewer than 50 employees and up to €10m.
Entreprise moyenne
Fewer than 250 employees, with turnover up to €50m or a balance sheet up to €43m.
Frequently asked questions
What SMEs ask us most often
Is beVault suitable for SMEs?
Yes, as soon as there are multiple sources to reconcile and a need for reliable reporting. The initial scope can remain limited; it is the method, not the size, that determines the result.
Can we start with a single domain or use case?
This is the very trajectory we recommend. A priority domain allows for the setup of the model, controls and initial marts before expanding.
Is a large data team necessary?
No, but available business contacts are needed to define the meaning of the data, and at least one technical profile on the client side. dFakto can supplement this arrangement for as long as necessary.
Can we keep our existing systems?
Yes. beVault connects to your sources and feeds your existing BI tools; it replaces neither your ERP, nor your CRM, nor your reporting tool.
Where can beVault be deployed?
On-premises, in your cloud, in PaaS or in hybrid mode, with Docker as the deployment technology. You keep control of the infrastructure and access.
What dFakto support is available?
Strategy and architecture, implementation, training, skills transfer, support and evolution of the model. These services are optional and scaled as needed.
How to prepare data for AI?
By first building a historised, controlled and documented foundation, then exposing Information Marts intended for specific uses. beVault does not provide an AI model.
What is the difference between beVault and a reporting tool?
A reporting tool displays data; beVault builds, historises, controls and documents the foundation on which this data rests, then feeds it into defined outputs.
Next step
See how beVault can integrate into your data trajectory.
Is your context specific?
Describe your sources, your team and your deadlines to us. We will tell you honestly where to start — and if beVault is the right tool for you.