Matching Platform for Digital Demands

DigitalHub.SH

The homepage of the matching platform, with the headline “Open Source Connects.” To the left and right of the page are a company profile and a matching details page.

What if public institutions and associations could post their digital needs, while companies shared their open source solutions on one platform and the right matches happened automatically?

  • 44

    matching criteria

  • 2

    user perspectives brought together

  • ∞

    reusability through open source

  • 44

    matching criteria

  • 2

    user perspectives brought together

  • ∞

    reusability through open source

Driving digitalisation in Schleswig-Holstein together

Across Schleswig-Holstein, there is no shortage of strong digital ideas, and just as many challenges faced by public institutions and non-profit organisations. What had been missing, however, was a simple way for the right people to find each other.

One of DigitalHub.SH’s goals is to bring together public administration, associations and the digital economy to make open-source solutions possible. To support this collaboration, we developed a digital platform that makes needs visible, identifies relevant expertise and automatically connects both sides.

The biggest challenge was designing a matching process that feels intuitive for associations and public institutions, avoids unnecessary technical jargon, and is still precise enough to connect them with the expertise of specialised companies.

  • A smartphone displaying the homepage of the matching portal. Ralf Hertzog is greeted, and below are listed the topics and projects that match his needs.
  • A laptop displaying the topic overview of the matching platform. There are two open topics and one draft.

An interdisciplinary approach to the perfect match

To develop the platform, we assembled an interdisciplinary team spanning UX/UI design, web development and software architecture, allowing us to move forward in a focused and effective way.

User research and strategy
What do associations and public institutions actually need, and how would they describe those needs? How do companies look for suitable projects? And what would a matching algorithm need to look like to bring both sides together while also protecting associations and public institutions from unsolicited sales approaches?

UX/UI design
We designed an interface that reduces complexity: clear language, intuitive steps and carefully defined matching criteria. The result is a platform that doesn’t overwhelm anyone, but helps everyone move forward.

Web development
The technical implementation was created through close collaboration between design and development: scalable, secure and modular. This makes the platform ideally suited for future extensions.

Software architecture
To ensure the platform remains viable in the long term, we built a sustainable technical foundation that supports openness and digital sovereignty. We publish the code on OpenCode, the German public administration’s platform for open-source software and its reuse. The UX concept is also aligned with KERN UX, the standards and design guidelines for user-friendly digital public services. In doing so, we support Schleswig-Holstein’s path towards greater digital sovereignty.

Web development
The technical implementation was created through close collaboration between design and development: scalable, secure and modular. This makes the platform ideally suited for future extensions.

Software architecture
To ensure the platform remains viable in the long term, we built a sustainable technical foundation that supports openness and digital sovereignty. We publish the code on OpenCode, the German public administration’s platform for open-source software and its reuse. The UX concept is also aligned with KERN UX, the standards and design guidelines for user-friendly digital public services. In doing so, we support Schleswig-Holstein’s path towards greater digital sovereignty.

Infographic explaining the matching process. Searchers and providers provide input on topic categories, principles, and budget. The matching engine calculates partial scores and a matching score based on this information.

Focused on precision

The matching algorithm is based on a multi-level scoring system: each piece of information within a topic is assigned a value that defines the maximum possible score for a provider. For every topic, the algorithm is run individually for each provider, resulting in a matching score that determines the order in which results are displayed.

Weighted topic categories / experience
Seekers select the categories relevant to their needs and can assign extra weight to specific areas. Providers indicate their level of experience on a scale. The algorithm compares both sides to determine how well they match in terms of expertise.

Principles
Both parties rank ten guiding principles by order of importance. The algorithm compares these rankings and assesses how closely their values align, making cultural fit visible — a key factor in successful collaboration.

Constraints
Requesters specify an available budget range, while providers state their minimum fee. If the figures do not match, the provider is excluded from the matching process.

The goal of the algorithm is not simply to compare data, but to create the most realistic and meaningful picture possible of how well a topic and a company fit together. By combining weightings, ranking comparisons and scale-based values, it creates a matching process that goes beyond a simple “yes/no” result. It captures both qualitative and quantitative factors and helps ensure that requesters and providers are connected when there is a genuine fit.

Principles
Both parties rank ten guiding principles by order of importance. The algorithm compares these rankings and assesses how closely their values align, making cultural fit visible — a key factor in successful collaboration.

Constraints
Requesters specify an available budget range, while providers state their minimum fee. If the figures do not match, the provider is excluded from the matching process.

The goal of the algorithm is not simply to compare data, but to create the most realistic and meaningful picture possible of how well a topic and a company fit together. By combining weightings, ranking comparisons and scale-based values, it creates a matching process that goes beyond a simple “yes/no” result. It captures both qualitative and quantitative factors and helps ensure that requesters and providers are connected when there is a genuine fit.

Two perspectives, one platform

From the outset, the matching system was designed to serve two distinct user groups equally well: seekers (associations and public institutions) and providers (companies). Each group comes with its own expectations, needs and ways of approaching the process. Seekers should be able to describe their needs easily and without requiring in-depth technical knowledge, while providers contribute their expertise from a professional and specialised perspective.

At the same time, the platform is not a sales channel. It preserves anonymity and protects potentially sensitive information from associations and public institutions. The decision to share contact details — and in doing so initiate a potential project — always remains with the seekers. Companies, on the other hand, can present themselves publicly on the platform and make their expertise visible.

Kelvin Leclaire Software Engineer

  • 44

    matching criteria

  • 2

    user perspectives brought together

  • ∞

    reusability through open source

Referenzen

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