At the core of Fact & Feeling lies a specialized engine of analytical algorithms that transforms architecture into a measurable and logical structure. Unlike conventional approaches, our analysis is not based on subjective assessments, but on a mathematically grounded system that understands architectural designs or built facilities as functional wholes.
The analytical architecture follows a clear dual logic:
On one side, a comprehensive classification of Architectural Variables (AVs) forms the foundation, capturing hundreds of parameters with precision—from spatial geometry to material properties.
On the other side, the definition of Health and Care Outcomes (HCOs) establishes the direct connection between spatial data and intended health effects. These include a structured classification of health criteria across the domains of physical health, psychological well-being, and performance, along with their respective subcategories.
The assessment of building performance is carried out through an in-depth systems analysis:
By accounting for direct and indirect as well as linear and non-linear interactions, the analysis makes clear that architecture is not a static construct. The system evaluates both immediate relationships and multi-layered dependencies to determine the actual impact of space on safety and orientation.
The modeling of compound effects reveals complex causal chains and cascading effects. This involves calculating how different factors reinforce or attenuate one another, allowing interactions—such as the influence of material selection on acoustics and the resulting stress levels—to be quantified with precision.
The application of multiple analysis formats provides a robust basis for decision-making through targeted root-cause analyses and gap analyses. In this process, the discrepancy between the current state and scientifically defined target values is identified in order to derive precise optimization potential for social interaction and operational efficiency within the facility.
The digital workflow is seamlessly integrated into the planning process, enabling the system to automatically identify all relevant variable classifications immediately after design data is entered. This instant algorithmic evaluation ensures that architectural designs can be assessed for their actual effectiveness and evidence base without delay.
A key advantage of our solution lies in its fully automated operation, which clearly distinguishes the high-precision digital analysis from traditional checklist-based methods. By eliminating human sources of error and relying on objective, mathematically substantiated results, the system enables transparent comparability and scientifically grounded evaluation rather than intuitive judgment.
Furthermore, the system remains future-proof by design: the engine is scalable and continuously updatable, allowing it to be flexibly adapted to different project sizes or specific requirements. The ongoing integration of the latest scientific findings ensures that the algorithms consistently reflect the current state of research, with new evidence immediately incorporated into every evaluation.
