As part of the employee exit in an organization, the clearance process is a mandatory requirement that guarantees that the employee leaves formally, returns all organization property, and gets the final paycheck. It is commonplace for this process to entail filling and submitting an exit clearance form. For each area of responsibility in the clearance process, an ascertainment of completion is marked by the use of signatures or clearance approvals from the requisite personnel. The review of literature showcased that this process often employs the use of physical forms, which means printing, filing, and tones of record keeping. On the other hand, some organizations use automated means, which are still largely human reliant, leading to delays, inconsistencies and lots of redundancies. The aim of this study was to develop an intelligent chatbot implementation for employee auto clearance using Deep Learning. A chatbot is an Artificial Intelligence (AI)-driven software tool that simplifies the interaction between humans and computers. Among many other advantages, a chatbot reduces the overall costs in mundane tasks, enhances the user experience and has greater availability. This research employed the qualitative design to explore the different ways and approaches that make up the clearance process, alongside their challenges, in formal organizations, within Nairobi, Kenya. The proposed deep learning chatbot model was developed using 2 hidden layers and trained on 2,000 epochs. The training data dictionary was categorized as tags, patterns and responses. The model was able to correctly match 99.91% of the input pattern data points to their corresponding response output data points, and where an input pattern seemed unclear, the model was able to respond accordingly. The model could successfully make the API calls to the web service, where digital signatures are appended, and finalize the exit clearance process with a complete and signed clearance form.