  We introduce a novel positional encoding strategy for Transformer-style
models, addressing the shortcomings of existing, often ad hoc, approaches. Our
framework provides a flexible mapping from the algebraic specification of a
domain to an interpretation as orthogonal operators. This design preserves the
algebraic characteristics of the source domain, ensuring that the model upholds
the desired structural properties. Our scheme can accommodate various
structures, including sequences, grids and trees, as well as their
compositions. We conduct a series of experiments to demonstrate the practical
applicability of our approach. Results suggest performance on par with or
surpassing the current state-of-the-art, without hyperparameter optimizations
or ``task search'' of any kind. Code will be made available at
\url{github.com/konstantinosKokos/UnitaryPE}.
