Textio is one of those tools that sounds amazing in concept: AI-powered software that analyzes your job descriptions in real-time and suggests changes to attract more diverse, qualified candidates. It scores your job postings based on performance data and tells you which phrases work and which don't.

But here's the question nobody wants to ask: does it actually make a measurable difference in your hiring outcomes, or is it just expensive autocorrect?

What Textio Actually Does

Textio analyzes job descriptions as you write them and provides a real-time score (0-100) indicating how effective the posting is likely to be. It highlights phrases that might discourage certain candidates, suggests alternatives, and predicts how quickly the role will fill.

The platform pulls from a database of millions of job postings and their outcomes (time-to-fill, applicant volume, diversity of applicant pool) to identify patterns. When you write "rock star developer," Textio flags it as masculine-coded language that may discourage women from applying and suggests alternatives.

What It Gets Right

The bias reduction is real. Research shows that gendered language in job descriptions does affect who applies. Phrases like "aggressive," "dominant," and "competitive" tend to attract more male applicants. Textio flags this stuff automatically and provides neutral alternatives.

Readability improvements help. Textio pushes you toward clearer, more concise writing. Job descriptions filled with corporate jargon and 47 required qualifications intimidate candidates. Textio encourages simpler language and shorter lists, which legitimately improves applicant volume.

Data-driven suggestions beat gut instinct. Instead of guessing which phrases work, Textio shows you actual performance data. If jobs with "comprehensive benefits package" fill faster than jobs with "competitive benefits," the platform tells you that. This is more reliable than just winging it.

It's fast. You can paste a job description, get feedback, and make improvements in 5-10 minutes. Compared to writing from scratch or endless revisions with hiring managers, this saves time.

Where It Falls Short

Now let's talk about the problems:

It's expensive as hell. Pricing isn't public (red flag), but expect $10K-30K+ per year depending on company size and features. That's a lot of money for software that helps you write job postings.

The suggestions can feel robotic. Sometimes Textio pushes you toward bland, generic phrasing to maximize score. You might end up with a job description that scores 95 but has zero personality or brand voice. High score doesn't always mean better quality.

It can't fix fundamental problems. If your job description asks for 10 years of experience with a 3-year-old technology, Textio will note that's unrealistic, but it won't fix your actual requirements. The tool improves writing—it doesn't fix bad hiring criteria.

ROI is hard to measure. Did you get more applicants because of better job descriptions, or because the market changed, or because you raised the salary, or because you posted on different job boards? Textio will claim credit for improvements, but isolating their actual impact is nearly impossible.

Learning curve exists. The interface isn't as intuitive as you'd hope. You'll spend time learning the tool and figuring out which suggestions to take seriously vs. which to ignore.

Does It Actually Work?

Here's where it gets interesting. Some companies report significant improvements after implementing Textio:

• Faster time-to-fill
• More diverse applicant pools
• Higher application completion rates
• Better quality candidates

But here's what you need to understand: most companies implementing Textio are also improving their hiring processes in other ways simultaneously. They're training hiring managers, revising compensation, improving employer branding. Is Textio responsible for better outcomes, or is it just one piece of a larger improvement initiative?

The honest answer is: it probably helps, but it's not magic. Better job descriptions should attract more candidates. Less biased language should improve diversity. Clearer requirements should increase application rates. But these improvements might be marginal, not transformational.

Who Should Actually Use Textio

Use Textio if you:

• Are a mid-size to enterprise company hiring significant volume (100+ roles/year)
• Have measurable diversity hiring challenges and budget allocated to address them
• Post dozens of job descriptions annually and need consistency across hiring managers
• Have $10K-30K/year budget for recruiting tools and can justify the spend
• Are serious about reducing bias in job postings beyond performative gestures

Don't use Textio if you:

• Are a small company doing low-volume hiring (under 30 roles/year)
• Have tight recruiting budgets where $10-30K is a major line item
• Mostly hire from inbound applicants or referrals (job description quality matters less)
• Don't have diversity hiring as a measured priority
• Can't track applicant data well enough to measure whether it's working

The Free-ish Alternatives

Before you drop five figures on Textio:

Ongig Text Analyzer - Similar concept, generally cheaper. Analyzes job descriptions for bias and provides suggestions.

Gender Decoder - Free tool that checks for gendered language in job postings. Way less sophisticated than Textio, but it's free.

Hemingway App - Free writing tool that pushes you toward simpler, clearer language. Won't catch bias, but will improve readability.

ChatGPT + good prompting - Honestly, you can ask ChatGPT to analyze job descriptions for bias, improve clarity, and suggest alternatives. Not as polished as Textio, but wayyyy cheaper.

Just learn to write better job descriptions - Wild idea: read some guides on effective job description writing and apply those principles yourself. Free, and builds actual skill.

The Bottom Line

Textio is a well-designed tool that does what it claims: it improves job description quality and reduces biased language. For large companies hiring significant volume with diversity goals, it can justify its cost.

But for smaller companies or those with tight budgets, the improvements you get from Textio can largely be achieved through training, templates, and free tools. You won't get the real-time AI feedback, but you also won't spend $20K per year.

My advice: If you're seriously considering Textio, run a pilot first. Use it for 3-6 months on half your job postings and compare results to postings written without it. Track time-to-fill, applicant volume, and diversity metrics. If you see clear improvement worth the cost, keep it. If the results are marginal, invest that budget elsewhere.

Don't buy recruiting tools based on impressive demos or fear of falling behind. Buy them based on measurable ROI with your actual hiring needs. Textio might be worth it—but make them prove it with your data, not their case studies.