We propose a new dataset for detecting non-inclusive language in sentences in English. These sentences were gathered from public sites, explaining what is inclusive and what is non-inclusive. We also extracted potentially non-inclusive keywords/phrases from the guidelines from business websites. A phrase dictionary was created by using an automatic extension with a word embedding trained on a massive corpus of general English text. In the end, a phrase dictionary was constructed by hand-editing the previous one to exclude inappropriate expansions and add the keywords from the guidelines. In a business context, the words individuals use can significantly impact the culture of inclusion and the quality of interactions with clients and prospects. Knowing the right words to avoid helps customers of different backgrounds and historically excluded groups feel included. They can make it easier to have productive, engaging, and positive communications. You can find the dictionaries, the code, and the method for making requests for the corpus at (we will release the link for data and code once the paper is accepted).