A global rate equation can be constructed empirically from experimental data, without explicit considerations of the reaction mechanism or mass transfer effects. Traditionally, kinetic experiments include "flooding" or the initial rates method. While these methods are generally easy to analyse (as the data can be linearised), the results must be treated with caution, as they are either performed under non-synthetically relevant conditions, or cannot detect changes in reaction orders associated with more complex mechanisms, such as catalyst deactivation or product inhibition. In the past few decades, modern advances in process analytical tools and computing power have greatly accelerated the development of data-rich kinetic experiments under synthetically relevant conditions. In the late 1990's, Blackmond pioneered the development of Reaction Progress Kinetic Analysis (RPKA), which greatly streamlines the determination of rate laws from a series of "same excess" and "different excess" experiments. The experimental data can be manipulated in an electronic spreadsheet to obtain overlays between rate vs concentration plots to obtain individual reactant order. Later, this was expanded to "time adjusted" concentration vs time plot, which was further elaborated by Burés in 2016, into Time Normalization Analysis (TNA) for determining catalyst order; later in the same year, this was further extended to all reacting species, called "Variable Time Normalization Analysis" (VTNA), based on numerical integration of the rate equation. One of the most attractive features of these visual kinetic analysis tools is that rection orders can be derived by visual inspection of reaction profile overlay, without the need for complex mathematical calculations or bespoke software, hence making them more accessible to the synthetic chemistry community. In the last few years, the widespread popularity of Python as a general programming language has greatly accelerated the automation of machine-readable tasks. Recently, Hein and co-workers have developed a Python package "Kinalite", a simple API for performing VTNA. Kinalite requires kinetic data from each experiment to be imported as individual csv files, containing time-concentration data of different reaction species. The user selects a reaction species and two relevant experimental data to determine the reaction order automatically. For multiple species, or for more than two data series, this process must be performed sequentially, determining one species order at a time. The package presents the result as a plot of 'errors' associated with different order values for a specific reaction species, demonstrating the best order value. These individually calculated orders can be combined to yield the global rate equation. By removing the need for visual inspection, Kinalite removes human bias.