AI Employer Brand Audit: What Your Score Really Means

You ran the free OpenRole audit. You got a score. Maybe it was 34/100. Maybe it was 67. Maybe it was 12.

What does that number actually mean?

More importantly: Is your score good enough? And if not, what do you fix first?

This is a complete breakdown of how the AI Visibility Score works, what each factor measures, and how to interpret your results.

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What is the AI Visibility Score?

The AI Visibility Score is a composite metric (0-100) that measures how accurately and frequently AI models represent your employer brand.

It's not a vanity metric. It's a predictive indicator of how many candidates you're losing to AI misinformation.

Formula:

AI Visibility Score = Σ (Factor Weight × Factor Performance)

We measure 7 factors, each weighted by impact on AI citation rates (based on analysis of 300,000+ domains and 680M AI citations).

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The 7 Factors (And Their Weights)

Let's break down each factor.

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Factor 1: Structured Data (25 Points)

What we check:

• Does your homepage have Organization schema (JSON-LD)?
• Does your careers page have FAQPage schema?
• Do your job listings have JobPosting schema with salary data?

Scoring:

• Organization schema present: +10 points
• FAQPage schema present: +8 points
• JobPosting schema with salary data: +7 points

Why it matters:
Structured data is the only format AI models reliably parse as authoritative. Without it, AI guesses. With it, AI cites your facts.

Evidence:
Companies with complete schema markup had 32% higher citation rates and 40% better factual accuracy in AI responses.

How to improve:

• Add Organization schema to your homepage
• Add FAQPage schema to your careers page
• Add JobPosting schema to job listings

Typical score:

• 0-5: No structured data (91% of employers we audited)
• 6-15: Partial implementation (homepage only)
• 16-25: Full implementation (all three schemas)

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Factor 2: Content Format & Structure (20 Points)

What we check:

• Is your careers page in FAQ format?
• Do you have comparison tables (e.g., salary ranges, benefits)?
• Is content structured with proper headings (H1 → H2 → H3)?
• Are facts easily extractable (bulleted lists vs. prose)?

Scoring:

• FAQ format: +8 points
• Tables or structured lists: +6 points
• Proper semantic HTML: +4 points
• Clear, extractable facts: +2 points

Why it matters:
AI models prefer content that's easy to quote. FAQ format is 3.2x more likely to be cited than prose paragraphs.

Evidence:
Content formatted as Q&A or lists had 20-25% higher citation rates compared to marketing prose.

How to improve:

• Rewrite careers page with questions as H2 headings
• Add tables for salary ranges, benefits, locations
• Use bulleted lists instead of paragraphs where possible
• See examples in the Complete Guide

Typical score:

• 0-5: All prose, no structure
• 6-12: Some structure (headings, lists)
• 13-20: Full FAQ format with tables

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Factor 3: Bot Access (15 Points)

What we check:

• Does your robots.txt allow AI crawlers?
• Is your careers page in your sitemap.xml?
• Can AI bots render your careers page (server-side rendering)?

Scoring:

• AI crawlers allowed: +8 points
• Careers page in sitemap: +4 points
• Server-side rendering (no JS requirement): +3 points

Why it matters:
If AI can't crawl your site, it relies entirely on third-party sources (Reddit, Wikipedia, outdated articles). You lose control of the narrative.

Evidence:
Domains accessible to AI crawlers had 2.3x higher citation rates than those that blocked crawlers.

How to improve:

• Check yourcompany.com/robots.txt — remove blocks on GPTBot, ClaudeBot, Google-Extended
• Add careers page to sitemap.xml
• Ensure site renders without JavaScript (or use SSR)

Typical score:

• 0: Blocking all AI crawlers (43% of companies)
• 8-12: Allowing crawlers, missing sitemap
• 13-15: Full access + sitemap + SSR

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Factor 4: Brand Reputation Signals (15 Points)

What we check:

• Do you have a LinkedIn Company Page (complete)?
• Do you have a Glassdoor profile (claimed)?
• Do you have an Indeed Company Page?
• Do you have a Wikipedia page (if applicable)?
• Is your company mentioned in news/media (last 12 months)?

Scoring:

• LinkedIn (complete): +5 points
• Glassdoor (claimed): +4 points
• Indeed: +3 points
• Wikipedia or major news coverage: +3 points

Why it matters:
AI models cross-reference. If three sources say the same thing about you, AI trusts it. If only your website says it, AI treats it as unverified.

Evidence:
Companies with 4+ verified profiles had 2.8x higher accurate citation rates and 64% fewer hallucinations.

How to improve:

• Complete your LinkedIn Company Page (full About section, logo, employee count)
• Claim your Glassdoor profile (even if you can't control reviews)
• Create an Indeed Company Page
• Build media presence (PR, guest posts, speaking)

Typical score:

• 0-4: No profiles or incomplete
• 5-10: 2-3 platforms, partially complete
• 11-15: 4+ platforms, fully populated

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Factor 5: Careers Page Quality (15 Points)

What we check:

• Is your careers page indexed by Google?
• Does it have unique, descriptive meta title and description?
• Does it load fast (<3 seconds)?
• Is it mobile-friendly?

Scoring:

• Indexed by Google: +6 points
• Unique metadata: +4 points
• Fast load time: +3 points
• Mobile-friendly: +2 points

Why it matters:
If Google can't find your careers page, AI can't either. This is foundational.

Evidence:
Pages with proper SEO metadata were 1.8x more likely to be retrieved by AI during RAG (Retrieval Augmented Generation).

How to improve:

• Check Google: site:yourcompany.com/careers
• Add unique title and meta description
• Optimize images, enable compression
• Test mobile rendering

Typical score:

• 0-5: Not indexed or severely broken
• 6-10: Indexed but poor metadata/performance
• 11-15: Fully optimized

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Factor 6: Salary Transparency (10 Points)

What we check:

• Do you publish salary ranges on job listings?
• Are ranges in a machine-readable format (JSON-LD JobPosting schema)?
• Are ranges realistic (not "competitive" or "DOE")?

Scoring:

• Salary ranges published: +5 points
• In JSON-LD JobPosting schema: +3 points
• Specific numbers (not vague): +2 points

Why it matters:
"What does [Company] pay?" is the #1 candidate query to AI. If you don't publish ranges, AI guesses — and usually underestimates by £20K+.

Evidence:
Companies with published salary data in schema had 87% fewer salary hallucinations in AI responses.

How to improve:

• Add salary ranges to job listings
• Implement JobPosting schema with baseSalary field
• Use real numbers (£75K-£95K), not "competitive compensation"

Typical score:

• 0: No salary data published
• 5-7: Ranges published but not in schema
• 8-10: Full transparency with schema markup

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What Score Do You Need?

Based on our analysis of 500 UK employers, here's the distribution:

Average score across all 500 companies: 34/100

Translation: Most employers are in the "Poor" range. If you're above 50, you're already ahead of 70%+ of companies.

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Competitive Benchmarks by Industry

Why tech scores higher:
Tech companies are more likely to have:

• Developer-friendly careers pages (already using schema.org)
• Salary transparency (norm in tech recruiting)
• Strong LinkedIn/GitHub presence

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What to Fix First (By Impact)

If you have limited time/budget, prioritize this way:

Priority 1: Structured Data (25 Points Available)

Time: 4-8 hours
Cost: £500-£2,000 (one-time dev work)
Impact: Highest ROI — immediate improvement in factual accuracy

Actions:

1. Add Organization schema to homepage
2. Add FAQPage schema to careers page
3. Add JobPosting schema to top 3-5 roles

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Priority 2: Content Format (20 Points Available)

Time: 2-4 days
Cost: £1,000-£3,000 (content rewrite)
Impact: High — dramatically improves extractability

Actions:

1. Rewrite careers page in FAQ format
2. Add salary table
3. Add benefits table

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Priority 3: Bot Access (15 Points Available)

Time: 30 minutes
Cost: £0
Impact: Medium-High — enables retrieval

Actions:

1. Check robots.txt — remove AI crawler blocks
2. Add careers page to sitemap.xml
3. Verify server-side rendering

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Priority 4: Salary Transparency (10 Points Available)

Time: 1-2 hours
Cost: £0 (policy decision)
Impact: Medium — eliminates salary hallucinations

Actions:

1. Publish salary ranges on job listings
2. Add to JobPosting schema

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Priority 5: Multi-Platform Presence (15 Points Available)

Time: 2-3 days
Cost: £0 (time only)
Impact: Medium — builds cross-reference trust

Actions:

1. Complete LinkedIn Company Page
2. Claim Glassdoor profile
3. Create Indeed Company Page

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Score Improvement Timeline

Based on clients we've worked with, here's a realistic timeline:

Month 1:

• Implement structured data → +20 points
• Fix bot access → +10 points
• New score: +30 points from baseline

Month 2:

• Reformat careers page → +15 points
• Publish salary data → +8 points
• New score: +23 additional points

Month 3:

• Complete multi-platform profiles → +12 points
• Optimize careers page SEO → +8 points
• New score: +20 additional points

Total improvement over 3 months: +73 points

Example:

• Starting score: 28/100 (Poor)
• After 3 months: 101/100 → capped at 100 (Excellent)

Typical result: 28 → 85 in 90 days.

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FAQ About the Score

Q: Is 100/100 achievable?
A: Yes. 2% of companies in our database have scores of 81+. All have full structured data implementation, salary transparency, and strong multi-platform presence.

Q: How often does my score change?
A: We recalculate weekly based on:

• Changes to your website (schema updates, content changes)
• Changes in AI crawler access
• New job listings
• External platform updates (LinkedIn, Glassdoor)

Q: Can my score go down?
A: Yes, if:

• You remove structured data
• You block AI crawlers
• Your careers page goes down
• You remove salary data

Q: How does this compare to Glassdoor ratings?
A: Completely different. Glassdoor measures human perception (employee reviews). AI Visibility Score measures machine perception (what AI models cite).

Q: What if I score 80+ but AI still gets things wrong?
A: Run a detailed audit. Scores measure infrastructure (schema, access, structure). They don't catch all factual errors. That's why we offer weekly AI monitoring for paid plans.

Q: Do competitors' scores matter?
A: Only for relative positioning. If you're in tech and your score is 45 but competitors are averaging 55, you're losing candidates to better AI visibility.

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Conclusion: Your Score is a Starting Point, Not a Destination

The AI Visibility Score tells you where you are today.

But what matters most is what you do with that information.

• Score 0-20: You're invisible to AI. Candidates asking about you get hallucinations. Fix bot access and add basic structured data this week.

• Score 21-40: AI knows you exist but guesses most facts. Add structured data and salary transparency this month.

• Score 41-60: AI has some accurate data. Fill the gaps with content formatting and multi-platform presence this quarter.

• Score 61-80: You're ahead of most competitors. Optimize for edge cases and maintain your lead.

• Score 81-100: You're in the top 2%. Focus on monitoring and continuous updates.

The gap between what AI could say (if you give it good data) and what AI actually says (based on guessing) is the opportunity.

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Want to check your score?
Run a free OpenRole audit — takes 30 seconds, shows your score and the exact factors bringing it down.

Then use this guide to prioritize your fixes. Check back in 30 days and see the improvement.

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Sources:

• OpenRole audit data: 500 UK employers (Feb 2026)
• Profound: 680M LLM citation analysis (2024-2025)
• Schema.org impact study: 300K domains (2025)
• Industry benchmarks: Internal OpenRole data (2024-2026)