Textio is an AI writing platform that gives HR teams real-time guidance to write fairer job posts and clearer performance feedback.
Textio is an AI writing platform built specifically for HR teams. It gives real-time guidance as you write job descriptions, candidate outreach, and performance feedback, flagging language that introduces bias and suggesting alternatives backed by outcome data. Textio’s model was trained on hundreds of millions of HR documents and their measurable results, so its recommendations reflect what actually works, not general writing advice. For teams that produce high volumes of recruiting or feedback content, Textio operates as an in-the-moment editor that makes every piece of writing better before it goes out.
Textio analyses language at the phrase level rather than checking for surface errors. It maps what you write against patterns in its training data and surfaces predictions about how that language will perform.
Textio does not publish pricing on its website. Plans start around $15,000 per year for organisations with fewer than 200 employees. For larger deployments, annual contracts typically range from $40,000 to $90,000 for 200 to 500 employees, and exceed $150,000 for enterprise organisations covering 1,000 or more employees. Prospective buyers need to contact sales for a custom quote.
Textio Loop focuses on recruiting content: job descriptions, candidate outreach messages, and employer brand copy. It analyses language in real time and suggests edits that improve applicant quality and diversity. Textio Lift focuses on performance management: it guides managers to write clearer, fairer performance reviews and feedback documentation, reducing rating bias and improving the usefulness of feedback for employees.
Textio trained its AI model on hundreds of millions of job postings and their outcomes, including which language patterns attracted or deterred specific candidate demographics. As you write, Textio scores each phrase against this data and highlights language associated with lower diversity outcomes. It then suggests specific alternatives rather than flagging problems without solutions.
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