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We build fit-for-purpose business platforms for end clients, or for IT solution providers and system integrators. We build full-scale turnkey platform solutions, addons, plugins, engines and other subsystems that can be integrated with existing platforms.

Not three wishes, but seven services.

Take a closer look!

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In 2011, Gartner predicted that within 10 years, the majority of global enterprises would support enterprise architecture (EA) as a distinct discipline that is integral to business planning.

EA has grown from a support function into a highly strategic one, responsible for designing an intelligent information architecture that supports digitalization and innovation. EA continues to evolve at a rapid pace as emerging technologies lead to business disruption.

We study and practice Enterprise Architecture, TOGAF maturity level III, since 2010. In a fit for purpose way, focused on business value and expected results at stake. A business led architecture approach. Business and project purpose, agility and ‘time to market’ are key.

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‘Event driven’ is a concept that is instantly recognizable by business leaders. An event is something which happens externally or internally with a direct impact on the business.

We have recognized the importance of EDA many years ago since Gartner published its Operational Intelligence concept. The way companies can respond to events is crucial to derive maximum value in business.

So, we accept that events happen; events drive business; and decisions drive value. And we will help you to ensure that it’s effective in the business solutions we design and develop.

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With our Knowledge engineering competencies we design and build real-time decision support systems that facilitate the augmented decision making process.
Knowledge engineering is a field of artificial intelligence (AI) that creates rules to apply to data to execute the thought process of a human expert.

Knowledge sourcing methods are essential to validate the structure, the content and the context of data, often from multiple data sources.

We use a modeling process to create protocols and knowledge rules that are executed on validated data by a knowledge rule engine.  In this way we support experts with a system following the same path for reasoning and using the same information sources.

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Welcome to the microservices era. We all know that Application Architecture is shifting from monolithic enterprise systems to flexible, event-driven approaches.

We studied and practice Microservice design and development and know that its crucial part of DEVOPS.

Piece services together in event-driven systems using richer, brokered protocols is a competency available in our studio.

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Business engineering services are a necessity to lead business innovations.

Delivering new insights by operating on the cutting edge of science and engineering.

Looking at problems from both the academic and practical angle.

Identifying value and design potential solutions that have a positive impact on new (lines of) business. All available in our studio and based on practical knowledge and experience in applying the specific business architectures and models, design, lean manufacturing principles and data science competencies.

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Systems engineering is a discipline based on requirements and all considerations pertaining to analyzing and managing them. The data ecosystem is the core part in our view.

We practice Systems Engineering first of all to sketch the system context diagram in which all systems supporting the stakeholders in business and in the project.

In parallel we practice system engineering to support project management. The project manager is responsible for project outcomes as well as the time, cost, and resources required to meet the requirements of both the product development and the entire project investment.

Working together, the project manager and the systems engineer ensure all stakeholders will be satisfied when systems are implemented in customers business operations.

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AI engineering is an emergent discipline focused on developing tools, systems, and processes to enable the application of artificial intelligence in real-world contexts. The rise in availability of computing power and massive datasets have led to the creation of new AI models and algorithms encompassing thousands of variables and capable of making rapid and impactful decisions. Too often, though, these capabilities work only in controlled environments and are difficult to replicate, verify, and validate in the real world and businesses.

We recognized the need for an engineering discipline to help teams to deliver AI and data science solutions implemented in business operations.