In a new attempt to overcome empiricism in establishing the elementary processes considered e.g. in traditional lookup table kMC, we here present a versatile workflow that is motivated by adaptive kMC but differs in two central aspects. First, we separate the process exploration step from the actual kMC simulation. This allows us to select the system's atomic configurations on which TS searches are conducted beyond the confines of the kMC algorithm. If the process list resulting from the automated exploration step motivates a suitable lattice mapping, our separated workflow furthermore enables the use of existing optimized lattice kMC codes. Second, our initial automatized process exploration (APE) approach exploits a fuzzy machine-learning (ML) classification to specifically focus the TS searches around atoms within the selected system configurations that exhibit non-equivalent and hitherto unexplored local atomic environments. Simultaneously respecting fundamental symmetries and being agnostic to insignificant differences in atomic positions, this minimizes redundancies in the TS searches and allows application of our workflow to more complex systems.