We investigate a modular, solver-centric hybrid framework that inserts quantum subroutines into mature classical optimization pipelines. A data-driven process, supported by benchmarking and learning, selects and tunes classical and quantum heuristics from a portfolio to target bottlenecks in energy and logistics instances.