Intent recognition is a critical element of natural language understanding, as it enables AI systems and chatbots to understand the underlying goal or purpose of a user’s input. There are several intent recognition solutions available, including Amazon Lex, DialogFlow, and RASA. RASA stands out among these solutions due to its customizable, open source nature and powerful deep learning integration. However, the official availability of RASA to the public presents some challenges that need to be addressed. This research proposes an API for detecting intent in text. The API is based on a RESTful service-oriented architecture style, uses the RASA framework as an intent detection solution, and leverages Docker container technology to simplify and accelerate API deployment and configuration. Unit tests were performed to validate the proposed solution, and the solution was deployed in the cloud using Docker. These unit tests not only validated the results, but also played a key role in improving the overall quality and efficiency of the solution. Deploying the solution in the cloud on a Linode server as a Docker container demonstrated that using Docker could facilitate its deployment and use.