On-premise
Within your own infrastructure. Discuss capacity, networking, maintenance and responsibility for availability and recovery. An external model connection requires a separate assessment.
Data & AI platform by Data Analytics
ENABLE
ENABLE is the Data & AI platform by Data Analytics. It connects and structures business data, making it available with controlled access for dashboards, analysis and developing and sharing applications and AI solutions. Start with a specific information need; Data Analytics supports setup and management.
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01
One shared foundation, different applications
Finance, sales and operations often use different systems. The same question can therefore produce different answers. ENABLE starts not with a chatbot, but with sources, definitions and relationships between records. What counts as revenue? Which period is closed? How do you connect a customer, order and invoice without double counting? These agreements make information useful across teams.
A shared foundation allows a dashboard, analysis and new application to use the same meaning. You do not have to gather files and explain calculations again for every question. It still requires maintenance: changes to sources, definitions and permissions need to be tracked. A platform does not replace ownership of your business information.
02
From data to AI and useful products
First, relevant sources are connected and checked. Records are then structured in a data model with recognisable concepts. Next, you decide which users, groups and applications need access. This foundation supports reporting, investigating questions or developing a focused AI solution. The outcome can be a dashboard, an analysis tool or an application for an agreed workflow.
Example: management sees revenue rising while margins decline. Sales data alone cannot explain it. Combining orders, purchase costs and agreed margin definitions allows an analysis to show differences by product group. AI can help formulate a question or describe results; the controller checks the calculation and context. This is a hypothetical example, not a customer result or a working chat on this page.
03
Access and management are part of setup
Not every employee needs to see the same data or use the same AI tools. Discuss user management, groups, data permissions and application access separately. Define who owns definitions, who reviews an analysis and who may share an application. Logging and monitoring help track use, failures and changes, depending on the selected setup.
Data Analytics supports setup and management of this whole environment. The required controls depend on your data, processes and risks. An AI answer is not an automatic decision. Particularly for customer communications or source-system changes, agree in advance on human review and approval. Technical capabilities and organisational agreements need to align.
04
Choose hosting and model processing deliberately
On-premise, private cloud and managed cloud are possible hosting approaches. Consider management responsibilities, expertise, capacity, network access and recovery from failures. Where the platform runs does not by itself determine where all AI processing takes place. A local language model and access to an external AI tool through MCP are different choices.
With an external AI service, permitted context and results may be processed outside your organisation. Make explicit which data is passed on and under which conditions. MCP handles the connection to permitted data or tools; it is neither a language model nor a privacy guarantee. For more detail on restricted environments, see the existing Private AI Platform page.
05
Start with a question you can verify
Choose a question whose answer supports a discussion or decision. List the sources, the responsible employee and what a useful answer contains. Start with a limited scope and compare the outcome with an existing report or a checked source calculation. Measure, for example, completeness of required records and how often an answer needs substantive correction, without promising savings in advance.
An ENABLE demo is an opportunity to compare your information need with the platform approach. Discuss your ERP, CRM or accounting system, intended users and how you want to share results. You can then decide which setup, connections and support fit. The topics below help you explore further, for organisations in the Netherlands and Dutch-speaking SMEs in Belgium.
Three possible hosting approaches
Private cloud
A restricted cloud environment with access and management agreements. Check where data and models are processed; private hosting does not automatically mean local AI.
Managed cloud
Cloud hosting with management support. Agree on roles, monitoring, connections and responsibilities. Model services and their data processing remain explicit choices.
Frequently asked questions
Is ENABLE only an AI chat?
No. It starts with reliable business data and controlled access. Dashboards, analysis, applications and AI solutions are different uses of that foundation.
Do all sources need to be connected immediately?
No. First select the sources needed for your question. Availability, freshness and required connections are agreed in advance.
Does AI processing always stay internal?
That depends on model choice and setup. A local model differs from an external AI service. With external AI, account for shared context being processed outside your organisation.
Can Data Analytics support management too?
Yes, Data Analytics supports setup and management. Scope and responsibilities are agreed for your organisation, sources and applications.
Discuss your question with Data Analytics
Which sources, users and checks does your application need? Discuss your question in an ENABLE demo.
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