Traditionally, the optimization of a chemical reaction, the development kinetic models and optimization of analytical characterization parameters are undertaken independently. With this approach many overlapping tasks are performed in parallel, thus leading to long lead-times and inefficient manpower allocation. To overcome these redundancies, Sagmeister et al. developed a dual modelling approach using a single platform that seamlessly integrates the calibration of PAT, reaction optimization, kinetic modelling, and parametrizes a process model for scale-up in approximately 8 hours. Their platform consisted of a flow reactor connected to a inline FTIR. In addition, the platform has two valves that allows a stream of reagents or target product to bypass the reactor coil directly into the in-line FTIR to perform. Using this configuration the platform can perform a calibration of the reagent and product concentration through a standard addition method. Once the PAT is calibrated the platform performs dynamic experiments where the concentration of the reagents are ramped to explore the parametric space. Finally, using scientific programming language called Julia they use the collected data to fit the kinetic model parameters and perform in-silico optimization of the reaction parameters. The efficacy of the dual modeling approach was validated through the attainment of Pareto fronts for both amidation and alkylation reactions.