Why is my AI Readiness score low?
AI Readiness scores under 60 usually indicate a critical gap in one of five categories: bot discoverability, structured data, LLM communication (llms.txt), content accessibility, or cross-signal readiness. The category breakdown in your Radar audit identifies which one is dragging the unified score down.
Radar AI Readiness combines 5 signals into one score. A low unified score (under 60) almost always traces to a dominant gap in one specific category. The breakdown tells you which.
The 5 categories scored
| Category | Points | What it checks |
|---|---|---|
| Bot discoverability | 25 | robots.txt allows AI bots (GPTBot, ClaudeBot, PerplexityBot) |
| Structured data | 25 | Organization, Article, Product JSON-LD on key pages |
| LLM communication | 20 | llms.txt presence + completeness |
| Content accessibility | 15 | Server-rendered HTML, semantic structure, no JS-only content |
| Cross-signal readiness | 15 | Internal linking, sitemap completeness, freshness |
Highest-impact fixes by failing category
- Bot discoverability low — open robots.txt, remove Disallow for GPTBot/ClaudeBot/PerplexityBot/Google-Extended. Quickest win.
- Structured data low — add Organization JSON-LD to root layout. Add Article schema to blog pages. Validate via Rich Results Test.
- LLM communication low — ship a complete /llms.txt with brand identity, product list, key URLs, FAQ, and use policy.
- Content accessibility low — ensure server-side rendering, use semantic HTML (h1-h6, nav, article), avoid JS-only content rendering.
- Cross-signal readiness low — generate XML sitemap, link related pages internally, add visible "Last updated" stamps with Article.dateModified.
Expected timeline to lift the score
Bot discoverability and structured data fixes register within 24-48 hours of deploy. llms.txt and content accessibility take 7-14 days for AI engines to re-crawl and update. A site going from 45 to 75 in 30 days is realistic if all 5 categories are addressed.
Quick win pattern: bot discoverability + Organization JSON-LD + llms.txt usually moves the unified score by 15-25 points within 2 weeks. Start there.
How to re-baseline after fixes
Re-run Radar AI Readiness after each batch of fixes. The category breakdown shows which fixes moved the needle. Compare to the prior audit for delta tracking.
Want to take action on this?
Related questions
Why is not ChatGPT citing my B2B SaaS?
ChatGPT cites sites it treats as authoritative for a query. Most B2B SaaS sites lack three specific signals: structured data declaring entity identity, an llms.txt file, and citations from domains ChatGPT already trusts.
WhyWhy is my company invisible in AI search results?
AI engines surface companies they trust as authoritative for a query. Invisibility usually means one of three things: AI crawlers cannot access your site, your structured data does not identify your entity, or no high-authority source on the web mentions you.
ComparisonSchema.org vs llms.txt: which AI search signal matters most?
Both matter and they do different jobs. Schema.org JSON-LD identifies your entities (Organization, Product, Person) per-page for machine parsing. llms.txt is a site-level declaration of what your site is about, what content to prioritize, and how AI engines should use it. Ship both.
How-toHow do I get cited by Perplexity?
Perplexity cites sources it crawls + ranks for the live query. To get cited: allow PerplexityBot in robots.txt, ship answer-first content (FAQPage schema, BLUF paragraphs), and acquire citations from high-authority domains Perplexity already ranks.
