In summary, our presented chip-based microfluidic setup offers a robust platform for monitoring biocatalytic transformations in packed-bed microreactors, featuring an automated injection workflow with almost real-time online LC/MS detection. Furthermore, we integrated a modular chipHPLC-based unit, which serves as a low-consumption alternative to the conventional C18-column used initially and significantly accelerates the separation process, while reducing solvent consumption by over 80%. Our investigation focused on immobilising a vanadiumdependent haloperoxidase onto silica particles and packing them into microfluidic fused-silica chips. The performance of our newly established system was evaluated by employing a biocatalytic bromination as a model reaction. The covalent immobilisation strategy showed good stability while maintaining high enzyme activity during long-term monitoring experiments using only minute amounts of catalytic material (1-10 mg range). The setup allows for the sequential monitoring of multiple interconnected microreactors by external valve control through reactor position switching. We showcased this capability by testing various sample compositions of the model biotransformation across multiple continuous microreactors in a single automated run, including blank measurements bypassing the microreactor. The versatility of this approach allows for easy adaption to a parallel screening of multiple reactions or different immobilised catalytic materials in separate microreactors, making it suitable for various applications, including data feedback of optimised enzymes by directed evolution. Due to its deficient substrate consumption, the system is also well suited for studying APIs and derivatisations. By combining microfluidic approaches with extended setup automation and Python-based data evaluation, such data-rich systems could pave the way for more accessible connections to upcoming data science disciplines for more sustainable and efficient chemical processes.