The tender process by client organizations in the Digital governance domain is a well-established mechanism to procure cost effective and high quality products and services in a fair and transparent manner. On the bidder’s end, one of the significant steps involves an in-depth requirement analysis of the Request for proposal documents floated by the client in various aspects, which is a time-intensive task. By assessing these fundamental aspects from the service provider’s perspective, in the next stage, the bidder frames its bid to be submitted to the client for evaluation. This research proposes a framework to automate the bid development process in response to the Request for proposal for the software development projects in the Digital governance domain. The methodology involves Bidirectional Encoder Representations from Transformers transformation on a curated dataset, with noise reduction followed by meticulous feature extraction with an aim to tokenize the keywords and enhance the interpretability to streamline the selection process. Alongside, the model’s performance is rigorously monitored using key metrics to gauge the precision, accuracy, an F1-score and recall. The monitoring outcomes underscore the robustness and the effectiveness of the proposed model and its capability to streamline the Digital governance bidding process by increasing the accuracy and the efficiency of the bid document classification process.