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Textio sells AI-powered writing software that helps companies write fairer job posts and better performance feedback.
Textio is a software company that helps employers write better words. Its platform uses AI to review job postings and performance reviews as people type, flagging language that might turn off candidates or inject bias, and suggesting clearer, more inclusive alternatives. The pitch is simple: the words a company uses shape who applies and how fairly employees are treated, so getting those words right matters.
The original product focused on job posts. A recruiter drafts a listing, and Textio's AI scores it and coaches the writer toward language that attracts a wider pool of applicants. The company later built out performance feedback tools, helping managers write reviews that are specific, useful, and less likely to carry hidden bias. Customers are typically HR and talent teams at larger employers, who pay a subscription to use the platform across their hiring and review processes. Textio was founded in Seattle in 2014 by Kieran Snyder, a linguist with a PhD in the field, and Jensen Harris. The founding insight came from data: Snyder studied millions of job listings and found that certain words and phrases correlated with how long roles stayed open and who applied. That research-first approach became the company's calling card, and it has collected a large body of language data to train its models. It raised venture funding and grew into a recognizable name in the HR technology space.
The business sits in a crowded and fast-moving market. It competes with general-purpose AI writing tools and with HR software suites that add similar features, and the rise of cheap, powerful language models makes "AI writing help" a much less special thing than it was in 2014. Textio's bet is that its specialized data and focus on inclusive hiring and feedback keep it useful. Its strengths are deep domain data and a clear niche; its vulnerability is that bigger platforms can bolt on similar features, so it has to keep proving its specialized models are worth paying for.
Saved answer · last researched 15 Nov 2026