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B2B SEO is distinct from consumer SEO in ways that matter for technical configuration. Your buyers research solutions over weeks or months. Your highest-value content — case studies, whitepapers, solution comparisons — is often gated behind forms that make it invisible to search crawlers. Your thought leadership content carries authority only when Google can attribute it to named experts with verifiable credentials. And your audience increasingly uses AI assistants for vendor shortlisting before they ever contact your sales team, which makes AI crawler configuration not a nice-to-have but a genuine pipeline consideration.
seo.yatna.ai crawls your B2B website the way Google and AI assistants do — evaluating content depth, E-E-A-T signals, schema coverage, canonical integrity, and AI readiness across 7 weighted categories. The audit is built around the specific failure patterns that affect B2B organic visibility and lead generation.
Gated content not accessible to crawlers — Whitepapers, research reports, case study PDFs, and detailed guides locked behind lead capture forms are invisible to search crawlers. Google can't evaluate their content, extract their keywords, or assign authority to your domain based on them. A common B2B mistake is creating genuinely high-quality long-form content and then making it completely inaccessible to the crawlers that would rank it.
Solution pages targeting job titles rather than searchable queries — "Built for VP of Operations" is a compelling message for a buyer who's already on your site. It's not a query anyone types into Google. B2B solution pages frequently optimize for buyer persona language — role titles, internal jargon, organizational pain points described in corporate terms — instead of the actual queries buyers use when they're looking for solutions during the awareness and consideration stages.
Case study pages with thin content — A B2B case study with two paragraphs of results ("We increased X by 40%") and a quote is not a rankable page. It has no keyword depth, no searchable context, and nothing to differentiate it from the generic case study template every competitor uses. Case study pages that rank combine specifics: industry, use case, the actual problem described in language buyers search for, and enough narrative depth to serve as a genuine reference document.
No Article schema on thought leadership blog posts — B2B content marketing depends on thought leadership. Google evaluates that content's authority in part through Article schema — specifically, whether it has a named author with a credentialed bio page, datePublished, dateModified, and an Organization publisher. B2B blog posts published without Article schema receive no structured data credit for the E-E-A-T signals they represent.
Named authors with credentials missing from authoritative content — Google's E-E-A-T framework gives preference to content written by people with demonstrable expertise. B2B content published under a company brand ("The [Company] Team"), without named authors and without author bio pages showing credentials and experience, provides no E-E-A-T signal. Competitors who attribute content to named, credentialed individuals have a systematic advantage in competitive B2B content categories.
Pricing page with "contact us" CTAs — missed keyword opportunity — B2B pricing pages that say nothing about price ("Contact us for enterprise pricing") are legitimate from a sales strategy perspective. But they miss the organic opportunity of pricing-related keywords: "[category] software pricing," "how much does [solution] cost," "[solution] vs [competitor] pricing." A pricing page that discusses pricing factors, tiers, and what drives cost can rank for commercial intent queries even without listing specific numbers.
Long-form content lacking proper heading structure — B2B content often runs long — 2,000 to 5,000 words on complex solution topics. Without a clear H2/H3 hierarchy, Google can't extract the topical structure of the content for featured snippets, People Also Ask boxes, or structured content evaluation. A well-structured article with 8 H2s covering distinct subtopics will consistently outperform a wall of text of equivalent word count.
AI crawlers not configured — B2B buyers use AI for vendor research — Enterprise and mid-market buyers increasingly use AI assistants to build vendor shortlists before engaging with sales. "What are the best [category] platforms for [industry]?" "Compare [your product] and [competitor] for [use case]." If GPTBot, ClaudeBot, and PerplexityBot are not explicitly allowed in your robots.txt, you are absent from the vendor research phase of your own buyers' buying process.
No llms.txt — AI assistants may misrepresent the solution category — B2B products frequently sit at category boundaries — is it a workflow tool, an analytics platform, or a data integration product? Without llms.txt providing an authoritative description, AI assistants categorize your product based on whatever they've crawled, which may emphasize the wrong use case, use outdated positioning, or describe your product in a category frame that doesn't align with your ICP's search behavior.
LinkedIn integration scripts blocking render — B2B sites commonly embed LinkedIn Insight Tag and LinkedIn social sharing widgets. These third-party scripts, when loaded synchronously or render-blocking, delay First Contentful Paint and contribute to poor Core Web Vitals — particularly on slow mobile connections. Audit data consistently shows LinkedIn-integrated B2B sites with higher render-blocking JavaScript scores than sites without these integrations.
seo.yatna.ai scores your B2B site across 7 weighted categories:
robots.txt for GPTBot/ClaudeBot, llms.txt, schema structured for AI citationA typical B2B website audited on seo.yatna.ai returns results like this:
| Category | Score | Key Finding |
|---|---|---|
| E-E-A-T | 41/100 | 18 blog posts without named authors |
| Technical SEO | 66/100 | Gated content pages blocking crawlers |
| On-Page SEO | 57/100 | 5 solution pages under 400 words |
| Schema | 28/100 | No Article schema on any blog content |
| Performance | 63/100 | LinkedIn scripts blocking render |
| AI Readiness | 19/100 | No AI crawler directives; no llms.txt |
| Images | 61/100 | Case study images missing alt text |
| Overall | 51/100 | 23 actionable issues found |
Each finding links to the specific URL where the issue was detected and includes prioritized fix guidance — with recommendations sequenced by estimated pipeline impact.
Should gated content pages be indexed by Google? The landing page for gated content — the page with the title, description, and form — should be indexed. The gated asset itself (PDF, report) typically shouldn't be crawled, though some SEO strategies advocate for open-access research as a link building strategy. The audit flags gated landing pages that are accidentally blocked from crawlers.
How do I add E-E-A-T signals to existing B2B blog content?
Start with author attribution: add named authors to existing posts, create author bio pages with credentials, and implement Article schema linking to those bio pages. This is the highest-impact E-E-A-T intervention for most B2B sites. The audit report shows which posts are missing author attribution.
My B2B site gets low traffic. Should I fix technical SEO before content? Technical SEO fixes are often prerequisites for content to rank. If your pages have canonical issues, crawl blocks, or missing schema, even excellent content underperforms. The audit report prioritizes findings by impact, so you can fix the technical blockers first and then invest in content with confidence.
How do AI assistants use my content for B2B vendor recommendations?
AI assistants synthesize information from crawled web content to generate vendor comparisons and recommendations. Having AI crawlers allowed, having llms.txt, and having accurate schema all contribute to being represented correctly. The AI Readiness score in the audit measures your configuration against this standard.
Your B2B content strategy needs a technical foundation that matches its ambition. The audit identifies every gap — from missing author schema to AI crawler configuration — so your content investments convert into pipeline.
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