OUR ALGORITHM

Complexity can be simple.

The technology of matched.io is backed by graph-based self-learning algorithms and individual data models. 

graph.webp

Target/actual evaluation

The skills and objectives of a developer are compared to the requirements of a job.

Requirement analysis

Facts such as salary, starting date and distance to work are considered.

Non-functional matching

The personality and mindset of a developer are brought into line with the corporate culture.

Smart Graphs

Programming languages, frameworks, methods and syntax are put into relation.

Self-learning algorithm

Each user decision affects the future behavior of the algorithm. This increases the individual quality of the matches.

How does matching work?

The ideal match prediction is achieved through domain knowledge taking into account benchmarks, trends and forecasts. The full automation of the technology accomplishes objectivity and high scalability.

How do we handle user data?