Observed Signal · Jun 15, 2026 · Product Launch · Source: DEV Community · Impact: 1/5 · Sentiment: Positive
One-Call API to Check AI Search Visibility
A developer (Daniel Igel) published a small audit engine and web tool that scores whether a website can be crawled, parsed and cited by AI search assistants (e.g., ChatGPT Search, Perplexity, Google AI Overviews). The audit evaluates robots.txt access for AI crawlers (GPTBot, OAI-SearchBot, PerplexityBot, ClaudeBot), presence of llms.txt, JSON-LD structured data, and passage citability, producing a 0–100 score with per-category pass/warn/fail results and concrete recommendations. The API is keyless and callable via a POST to https://citeready-api.sprytools.com/v1/audit; a web UI at https://citeready.sprytools.com offers a free tier (three audits/day) without signup.
A niche technical tool that helps publishers and site owners optimise visibility to AI-driven search/citation systems; useful for SEO/GEO workflows but not industry-shifting.
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Key Takeaways & Evidence Grounding
- Author Daniel Igel built an audit engine that scores a URL 0–100 for AI search visibility across multiple categories.
- The tool checks AI crawler access (robots.txt rules for GPTBot, OAI-SearchBot, PerplexityBot, ClaudeBot), llms.txt, JSON-LD structured data, and citability.
- A keyless API endpoint example: POST https://citeready-api.sprytools.com/v1/audit with JSON body {"url":"https://example.com"}.
- Web version available at https://citeready.sprytools.com with a free tier of 3 audits/day and no signup required.
Connected Companies & Entities
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Related Market Signals & Shifts
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Free SEO + AI-Search (GEO) Audit Tool Released
Developer Ravi Gupta published a free, open-source SEO and AI-search (GEO) audit tool at freeseoaudit.vercel.app. The tool runs without signup or crawl limits and streams reports across 40+ checks in eight categories, including metadata, JSON-LD structured data, crawl & indexing, Core Web Vitals, security headers, and GEO/AEO-specific checks. GEO checks validate llms.txt, detect accidental blocking of AI crawlers (e.g., GPTBot, ClaudeBot, PerplexityBot), evaluate paragraph self-containment (the "Island Test"), and surface direct-answer and E-E-A-T signals. The app uses Next.js 15 and React 19 on Vercel, with a Node/Express TypeScript backend on Render and optional Playwright rendering; code is available on GitHub.
Firewalls determine AI crawler access — 18-site audit
An author built an open-source tool, geo-crawl-audit, to probe how major websites treat AI crawler user-agents and how much readable content exists in raw HTML before JavaScript runs. The probe (against 18 sites on August 7, 2026) found that many sites' firewall and bot-management rules — not robots.txt alone — determine which AI crawlers can fetch pages, that several prominent crawlers (e.g., GPTBot, ClaudeBot, PerplexityBot) do not execute JavaScript, and that some well-known sites either deliberately or inadvertently present almost-empty raw HTML to most AI crawlers. Five sites blocked the probe's baseline requests entirely, highlighting the difficulty of measuring crawler access from arbitrary networks. The author published the tool and a public scanner to help operators check AI readability of their domains.
12-Surface AI Visibility Audit for B2B Brands
This article by Megan Kessler (Founder & CEO, Dark Horse Strategies) presents a practical, step-by-step methodology to audit a B2B brand’s visibility across the specific sources LLMs and AI search draw from. The framework defines 12 distinct “surfaces” (AI interfaces, AI search, reviews, publishers, owned content, developer/technical platforms, social, knowledge graphs, marketplaces, case studies, community/Q&A and enterprise/private AI) and provides a worksheet, scoring rubric and timeboxed steps to run tests across LLMs, search results, review sites, knowledge-graph sources and social/community channels. It lists platforms to test (ChatGPT/GPT-4o, Perplexity, Gemini 1.5 Pro, Claude, Microsoft Copilot), gives logging templates and diagnostic rules, and recommends scoring each surface 1–10 to prioritise remediation. The full audit is estimated to take about three hours and supporting resources are available on GitHub.
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