More clarity. More evidence. Better decisions.
MAKOR places a layer of meaning over everything your organisation already has — and brings parallel areas of expertise together into a shared situational picture. Your systems stay. Decision depth grows.
No migration. No replacement. Weeks instead of years.
Every area of expertise produces its own picture. Sharp. Precise. True — within its dimension.
Picture your organisation: every department knows its own area precisely. Procurement knows the suppliers. Technical management knows operations. The security experts recognise every deviation that points to a danger. All these experts deliver the best that the systems behind them allow.
Each of these areas of expertise is highly specialised, but limited to its own domain. As soon as a question crosses domain boundaries, it reaches its limit:
“What is our actual starting point — now, complete, substantiated?”
The question has been asked, but now the real problem begins. One department pulls its data — as of last quarter. Another checks its area, but its system only knows the current state, not the planned development. A third has already made assessments — in another system, following another logic, evaluated by someone who no longer works at the company.
Days later an answer arrives. Pieced together from several exports, phone calls and spreadsheets that someone maintained “quickly”. But on what basis can anyone stand behind this decision? Every state applied at a different point in time. Every assessment followed its own logic, which nobody can fully reconstruct today.
The most dangerous part: the answer looks current. But no record carries an expiry date. No piece of information signals whether it is still valid today. The picture looks complete, but nobody can say which of its parts are already out of date.
Responsibility is distributed. Validity is invisible. A shared picture on which both can rely does not exist.
Flat individual views become a picture with depth.
Each area of expertise produces a layer. One dimension of the whole. Complete in itself and yet limited to its own section.
MAKOR lays these layers on top of each other. Connections that are not visible in any single dimension emerge. A risk that is rated low in one area changes its weight as soon as two further dimensions are added, from another system, another point in time, another logic. This connection always existed. It was simply never visible.
MAKOR brings parallel areas of expertise together into a shared basis for decisions.
From data integration to decision intelligence.
The idea of a shared data foundation is not new. In practice, however, implementing it was laborious: different systems, data models and technical terms had to be captured manually, standardised and linked with one another.
With modern AI methods, a large part of this meaning modelling can now be prepared automatically. System documentation, data structures and unstructured information can be analysed and classified by subject. At the same time, AI alone is not enough. Language models can open up information and recognise connections, but they do not automatically ensure that a statement is correct and traceable.
MAKOR emerged from this combination: a shared layer of meaning for existing systems. MAKOR combines artificial intelligence with firmly defined rules and provenance records in order to bring information together and provide reliable bases for decisions. Results are verified and remain traceable back to their source data.
What defines MAKOR.
MAKOR builds on your existing systems. That cuts the time to the first reliable result from years to weeks, and existing IT investments become connectable instead of worthless.
MAKOR substantiates every recommendation and makes its derivation traceable down to the individual source record. In regulated areas, a convincing analysis thus becomes a reliable basis for decisions. If the data history is not sufficient for a forecast, the platform says so instead of hallucinating. Likewise, it correlates only lawfully collected information, without collecting data itself.
MAKOR prepares decisions, prioritises, warns, substantiates. A human makes the decision; the platform does not intervene autonomously in processes. This is an ethical position and the precondition for responsibility to remain attributable.
MAKOR is developed, operated and hosted in Germany, without conflict with the access laws of third countries. The platform is based on open standards and thus creates long-term technological independence. Anyone who uses MAKOR loses neither data nor modelling, because dependency is not a business model.
Decision intelligence without compromise.
| Criterion | US platforms | Data warehouse | Integrators | AI assistants | MAKOR |
|---|---|---|---|---|---|
| Usable without migration | partly | no | no | yes | yes |
| Proof of provenance | partly | partly | no | no | yes |
| Statements rule-checked | no | no | no | no | yes |
| No third-country-law risk | no | partly | partly | no | yes |
| Replaceable without data loss | no | partly | no | yes | yes |
| Foundation for AI agents | partly | no | no | no | yes |
What matters is the combination: traceability and speed. Sovereignty and performance. Until now you had to choose. That is the gap MAKOR fills.
Leading in sovereign, verifiable decision intelligence in Europe.
Architecture, modules and audit trail in detail.
Start small. But do it properly.
One site. One clearly named use case. Real data. An agreed success criterion.
Not as a free demonstration, but as a paid project with a defined result, because only that produces a reliable statement about your own data situation.
weeks Timeframe
A question that nobody can answer today in a reasonable time. Access to the systems in which the answer lies. A department that thinks along.
A verified result on your real data, with a complete chain of provenance. And a reliable estimate of what the path to broad rollout would cost.
Six weeks from approval to the verified result. After that you decide on rolling it out more broadly.
Request Proof of ValueYour use case. Our assessment.
Describe your challenge — we will get back to you within two working days with a concrete analysis.
Ontology+
A binding dictionary of your own organisation — not just terms, but also their relationships and rules.
Semantic abstraction+
The translation layer that maps technical field names from legacy systems to business terms.
Knowledge graph+
Information as a network of things and their relationships — instead of a collection of tables.
Neurosymbolic AI+
The combination of a learning side that recognises patterns and a verifying side that checks every assumption against fixed rules.
Provenance · Traceability+
The unbroken chain of origin of a result, back to the source record.
Human-in-the-loop+
The design principle: the final decision always lies with a human.
AI agent+
A program that works through a task independently in steps — rather than merely answering.
Digital sovereignty+
The ability to control your own data and systems without depending on the legal system or the business model of a third party.