Augmented writing for job posts, performance reviews, and now interviews - built on the radical idea that bias hides in language, and language can be measured.
The wordmark, scored. Textio's whole product in one image: even a logo could, in theory, be graded for how it reads. Approximate illustration.
Somewhere right now, a hiring manager is typing the word "rockstar" into a job post. They mean it kindly. They want energy, talent, someone who shows up. And then a small panel on the right of the screen lights up and gently disagrees - that word, it reports, tends to pull a narrower crowd than they think.
That panel is Textio. It does not nag, and it does not autocorrect your spelling. It does something stranger and more useful: it predicts how the words will land before anyone reads them. The manager swaps "rockstar" for something plainer, watches a score tick upward, and moves on. The whole exchange takes four seconds. Multiply those four seconds across every job post, every performance review, and now every interview inside a large company, and you have a business.
Textio was founded in Seattle in 2014 by Kieran Snyder and Jensen Harris. On paper it is HR software. In spirit it is a linguistics experiment that escaped the lab. Snyder has a PhD in linguistics and spent years studying how word choice quietly sorts people - who applies, who feels addressed, who clicks away. Harris brought the product instincts. Together they built software that treats writing as data.
The category they coined is "augmented writing." The pitch is deceptively simple. Most workplace writing is high-stakes and done by amateurs. A job post decides who applies. A performance review can shape a career. Yet people write both with gut feel and inherited habit, never knowing how the words actually perform. Textio attaches a feedback loop to that act.
The first product zeroed in on one document with outsized consequences: the job post. Textio scored it, flagged gendered and age-coded phrasing, predicted response rates, and suggested edits in real time. The radical move was not the AI. It was the insistence that "good writing" at work could be measured at all, rather than admired in hindsight.
From there the thesis traveled. If language carries bias into hiring, it carries bias into feedback too. So Textio built tools for performance reviews. Then, in 2026, it pushed into the interview itself - the messiest, most subjective room in the building.
The core engine. Type a job post or message and Textio scores tone, clarity, and bias in real time, suggesting rewrites that widen - rather than narrow - your audience.
Helps managers write reviews that are specific and fair. It flags vague praise and biased language - the difference between "great attitude" and feedback a person can actually use.
Optimizes job descriptions and recruiting language to attract a broader candidate pool, fitting inside existing applicant-tracking workflows.
Turns interviews into consistent, comparable evaluations. Its Skills Matrix maps what a candidate actually said against what the role requires. Free to start.
A simplified, illustrative look at the kind of swap Textio surfaces. The point is never grammar - it is who the words include, and how specific they really are.
Illustrative examples only - not exact Textio output.
Backers include Emergence Capital, which led the Series A, plus Scale Venture Partners, Cowboy Ventures, Bloomberg Beta, Upside Partnership, Industry Ventures, and Operator Collective. The roster reads like a who's-who of enterprise-software conviction - investors who like durable categories over viral spikes.
Textio's customers are the HR, recruiting, and people teams inside large organizations - the ones who write at scale and live with the consequences. Over the years its named users have included Johnson & Johnson, Cisco, Starbucks, and Barclays.
These are companies that publish thousands of job posts and run countless reviews a year. A small, consistent nudge on each one compounds. That is the business case in a sentence: not a dramatic rewrite, but a reliable thumb on the scale toward clearer, fairer language - applied everywhere, every day.
Textio runs an active YouTube channel with product walkthroughs and talks on language, bias, and hiring. A good place to watch the panel-on-the-right idea actually work.
Return to that hiring manager and the blinking cursor. A decade ago, "rockstar" would have sailed straight into the post, and a chunk of qualified people would have quietly decided the job was not for them. No one would have known why. The applications simply would have looked the way they always looked.
Now the word gets a second look before it leaves the building. Not because the manager became a better writer overnight, and not because a machine took the pen away - but because the gap between intention and effect got a number attached to it. That is the small, stubborn thing Textio changed: it made the invisible cost of a careless word visible, four seconds at a time. The cursor still blinks. The argument about the single word is still quiet. It just no longer happens in the dark.