A job ad is the very first thing candidates see, so it’s crucial to get it right. If not, you risk losing the best candidates before they even apply. Biased language and other non-best-practice writing create friction for certain groups within your qualified candidate pool. Companies see a much higher flow of qualified candidates by removing this friction and communicating inclusively.

Our software helps companies by analyzing and flagging biased language and other problem areas in their job ads, then suggesting more effective alternatives. These alternatives have been proven to reduce bias and friction, making job ads more appealing to the right candidates.

"‘We are looking for a strong contributor with an impressive drive’ is an example of a sentence that doesn't speak to all candidates. It has been statistically proven that words like "drive" and "strong" can exclude certain groups of people because they subconsciously conclude that the company expects a different type of candidate. Finding alternative words and phrases will bring you more qualified applications." Linea, Linguist, Develop Diverse

Our software relies on scientific research, tons of data, and AI. We take the guesswork and subjectivity out of job ads, making them more effective and high-performing - fostering a more merit-based approach to the hiring.

Even better? It’s easy to use and implement. Our editor tool gives each job advert a score and highlights biased language, clichés, or stereotypes. With just one click, you can replace them with fairer, more effective alternatives. And this isn’t based on gut feelings - our AI is continuously trained by linguists using the latest research in the field and large amounts of data.

You can also collaborate with your team, track performance, and even integrate our editor directly into Microsoft Word. Plus, with our Teamtailor integration, syncing high-quality, bias-free job ads takes just one click.  

In the end, fairer, merit-based hiring leads to better candidates, better teams, and better company performance.