In this work, a workflow was proposed towards reaction completion of existing reaction data records. Given a reaction record, either from reaction SMILES, or other reaction representations, a proposed heuristic tool, ChemBalancer, first checks if the reaction is a complete reaction, or incomplete from the left hand side (LHS) or the right hand side (RHS) of the reaction equation. The ChemBalancer intends to add one (or sometimes more than one) specie into a side (or sometimes two sides) of reaction accordingly, with the aid of reaction atom mapping tool RXNMapper. If the ChemBalancer fails to complete the reaction record, the reaction SMILES string is considered as a language of chemistry and is passed into a finetuned masked language model (MLM), a BERT transformer, originally designed to detect missing words in a sentence based on its context, to infer the most possible missing molecule in the reaction. Here, the MLM model, ChemMLM, was trained from a set of completed reactions detected by ChemBalancer, to predict the missing molecules in a reaction. This is followed by using ChemBalancer to further check if the updated reaction is completed. Every step of this pipeline intends to only add one most possible molecule to the incomplete reaction, and the specific sequential design of this pipeline was to learn only from available complete reactions and maximise the likelihood to propose correct molecules for incomplete reactions towards reaction structure completion.