Job descriptions are where recruiting dreams go to die. You write what you think is a clear, compelling post. You get 12 applications, 11 of them completely unqualified, and the one good candidate ghosts you after the first conversation.

Textio promises to fix this with AI-powered writing guidance. Their pitch: paste your job description into their platform, get real-time suggestions to improve language and structure, and post jobs that perform measurably better.

The platform has some impressive customers—Microsoft, Twitter, and Atlassian reportedly use it. But does the AI actually make your job posts better, or is this just spellcheck with a fancy interface?

I tested Textio on 30 job descriptions across engineering, sales, and operations roles. Here's what actually works.

What Textio Actually Does

Textio is a writing enhancement platform specifically trained on recruiting language. You paste your job description into the editor, and it analyzes the text in real-time, providing:

1. A "Textio Score" (0-100) predicting how well your post will perform 2. Specific language suggestions to improve clarity, inclusivity, and appeal 3. Bias detection highlighting problematic language around gender, age, or other factors 4. Phrase alternatives based on what actually works in successful job posts

The platform is trained on millions of job posts and hiring outcomes, so suggestions are supposedly based on real data about what language correlates with better candidate quality and higher application rates.

That's the theory. Let's talk about the reality.

The Textio Score: Useful But Imperfect

Every job description gets a score from 0-100. Higher scores supposedly predict better performance—more applications from qualified candidates.

According to Textio's own research, jobs scoring 90+ fill 25% faster than jobs scoring below 50. That's compelling if true.

My experience over 30 job posts:

• Initial scores ranged from 42 to 78 before any edits
• Posts scoring 70+ did see slightly better application quality (but correlation ≠ causation)
• Posts scoring 90+ (after implementing suggestions) didn't dramatically outperform posts in the 70-80 range

The score is a useful guide, but don't obsess over hitting 100. A post scoring 75 with authentic language probably outperforms a post scoring 95 that sounds like it was written by a robot.

Language Suggestions: Hit or Miss

Textio flags specific phrases and suggests alternatives based on performance data. Here's what I found:

What Actually Helped

Replacing vague language with specifics:

• "Fast-paced environment" → "You'll manage 3-5 active projects simultaneously"
• "Strong communication skills" → "You'll present weekly updates to executive stakeholders"

These suggestions genuinely improve clarity. Research confirms that specific language attracts more qualified candidates.

Cutting jargon:

• "Synergize cross-functional deliverables" → "Coordinate with product and engineering teams"
• "Best-in-class solutions" → "Software tools that solve customer problems"

Good catches. Corporate buzzwords make candidates' eyes glaze over.

Shortening long sentences: Textio flags sentences over 25 words and suggests breaking them up. This actually improves readability, especially on mobile devices where 70% of job seekers browse opportunities.

What Was Questionable

Over-optimization for "excitement": Textio loves words like "exciting," "incredible," "amazing." But using them everywhere makes your post sound like a used car commercial.

Their suggestion: "Join our incredible team building amazing products!" Better version: "Join a team that launched three products reaching 1M+ users this year."

Data is more compelling than adjectives.

Bias alerts that felt pedantic: Textio flagged "guys" (as in "our engineering guys") for gender bias—fair point. But it also flagged "recent graduate" as age-biased and "digital native" as potentially problematic.

Some of these catches are valid. Others feel like the platform erring on the side of flagging everything, leaving you to sort out what matters.

Generic phrase replacements: Sometimes Textio suggests replacing distinctive language with bland alternatives. Your authentic voice matters more than marginal score optimization.

Bias Detection: Genuinely Useful

This is where Textio provides real value. The platform highlights language that research shows correlates with lower diversity in applicants:

Gender-coded language:

• "Competitive," "dominant," "rockstar" → More male applicants
• "Collaborative," "supportive," "team-oriented" → More female applicants

Studies confirm that gendered language affects who applies, even when the language doesn't explicitly exclude anyone.

Age-coded language:

• "Digital native" suggests you want young people
• "Recent graduate" excludes experienced candidates
• "Energetic" can be code for young

Exclusionary requirements:

• "Must have" language reduces applications, especially from women and underrepresented groups
• Requiring 100% of qualifications filters out diverse candidates who apply only when they meet all requirements

Textio catches these patterns and suggests more inclusive alternatives. This feature alone justifies the platform for companies serious about improving diversity.

Real-World Testing: The Results

I ran a controlled test with 10 pairs of similar jobs (same role, different teams). Half used Textio-optimized descriptions, half used our standard descriptions.

What I measured:

• Application volume
• Candidate quality (based on resume screening)
• Time to fill
• Diversity of applicant pool

Results after 6 weeks:

Application volume: Textio-optimized posts got 17% more applications on average Candidate quality: No significant difference—about the same percentage of qualified candidates Time to fill: 15% faster for Textio-optimized roles (but this could be due to higher volume giving more options) Diversity: 23% more applications from underrepresented groups for Textio-optimized posts

The diversity improvement is the most significant finding. Better language genuinely affects who feels included in your candidate pool.

The User Experience: Pretty Smooth

What works:

• Clean interface that doesn't feel cluttered
• Real-time feedback as you type
• Easy to accept or dismiss suggestions
• Can save templates for similar roles
• Integrations with major ATS platforms like Greenhouse, Lever, and Workday

What's annoying:

• Sometimes the platform lags when analyzing long job descriptions
• Can't easily compare multiple versions side-by-side
• The constant score updating can be distracting—you become fixated on the number instead of the content

Learning curve: Minimal. If you can use Google Docs, you can use Textio.

Pricing: Enterprise-Level

Textio doesn't publicly share pricing, which tells you it's expensive. Based on G2 reviews and conversations with their sales team:

Estimated pricing:

• $10,000-20,000+ per year for small teams (5-10 hiring managers)
• Custom enterprise pricing for larger organizations
• Usually requires annual contracts

That's a significant investment for a writing tool. Whether it's worth it depends on your hiring volume and budget.

Who Should Use Textio

Good fit:

• Companies posting 50+ jobs per year
• Organizations with diversity and inclusion goals that need help with inclusive language
• Large recruiting teams where consistency across job posts matters
• Companies willing to invest in employer brand and candidate experience

Not a good fit:

• Small companies with limited hiring (a few roles per year)
• Teams without budget for recruiting tools beyond basics
• Companies that already write clear, inclusive job descriptions (you probably don't need AI to tell you what you're doing right)

What Textio Gets Right

Bias detection is genuinely valuable. If improving diversity is a priority, Textio helps catch language that inadvertently excludes candidates.

The specific language suggestions beat vague corporate speak. Pushing for clarity and specificity improves job posts regardless of the score.

The data-driven approach is sound. Textio's suggestions are based on actual performance data, not just best practices someone made up.

What's Overhyped

The Textio Score isn't magic. A post scoring 90 isn't guaranteed to outperform one scoring 75. Content quality still matters more than optimization.

AI can't replace strategic thinking. Textio can improve your writing, but it can't fix fundamental problems like unclear role expectations or uncompetitive compensation.

Improvement is incremental, not revolutionary. In my testing, Textio-optimized posts performed 15-20% better. That's valuable but not game-changing.

Better Alternatives If Budget Is Tight

If Textio's pricing is out of reach, here are cheaper alternatives:

Ongig Text Analyzer - Free bias checking tool Gender Decoder - Free tool for gender-coded language Hemingway Editor - $20 one-time for readability improvement Just ask diverse colleagues - Free and often more insightful than AI

These won't provide Textio's comprehensive analysis, but they'll catch major issues at a fraction of the cost.

The Bottom Line

Textio is a legitimately useful tool for companies posting lots of jobs who want to improve language quality and inclusivity. The bias detection features are valuable, and the platform does make it easier to write clearer job descriptions.

But it's not magic. You'll see incremental improvements—more applications, slightly better diversity metrics, marginally faster time to fill. Whether that's worth $10,000+ per year depends on your hiring volume and priorities.

My recommendation:

If you're posting 50+ jobs per year and diversity is a priority, Textio is worth the investment. Request a trial, test it on real jobs, and measure actual results before committing.

If you're hiring occasionally or have tight budget constraints, use free bias-checking tools and invest more effort in writing clear, specific job descriptions manually. Good writing principles work regardless of whether AI is involved.

Textio is a helpful writing assistant, not a recruiting strategy replacement. Use it as one tool among many, not a silver bullet for hiring challenges.