donottrack.us shows strong AI visibility with an ACRI of 74/100, outperforming 78% of indexed domains. Its server-rendered architecture ensures AI crawlers receive complete HTML on first request, a key advantage for extractability. A 5.2× bloat ratio is typical for sites in this tech tier — not wasteful, but streamlining could further boost extractability. Zero schema blocks puts this site at a disadvantage in knowledge graph and AI-answer pipelines that rely on explicit structured data. The site maintains an open-door policy for AI crawlers — GPTBot, ClaudeBot, and other major agents are all allowed.
🧮 Score Transparency — How is this calculated?
📊 ACRI Sub-Scores (AI Readiness Detail)
donottrack.us is more visible to Google than to AI models. There's room to improve AI discoverability to match your search reputation. ACRI measures technical crawler readiness. Read the methodology →
Why donottrack.us ranks here
Fastest improvements
- Add basic Organization and WebSite JSON-LD to fix “0 schema blocks” (see Schema Coverage).
- Reduce token bloat (navigation/footer/code) so agents reach your main content faster (see Token Bloat).
- Create an
llms.txtfile so AI crawlers can discover your content structure without heavy crawling. Generate llms.txt → - Run a full entropy audit to find which DOM regions waste the most tokens. Run Entropy Audit →
Traditional SEO
39/100 25 % of Global Score 🟢 High Confidence📝 Title Tag
Optimal range: 30–60 characters for SERP display.
📋 Meta Description
Optimal range: 120–160 characters for snippet control.
🔤 Heading Hierarchy
- ✗ Exactly 1 <h1> tag — found 2
- ✓ Has <h2> headings — found 12
- ✓ <h2> not before <h1>
🔍 Indexability
- ✓ Canonical tag present →
https://www.eff.org/issues/do-not-track - ✓ No noindex directive
- ✓ Meta viewport set
- ✓ HTML lang attribute →
en - ✅ Hreflang tags
- ✓ Googlebot allowed by robots.txt
🌐 Social / OpenGraph
- ✓ og:title — Do Not Track
- ✓ og:description — What is online tracking? Online advertising is big business. According to industry groups, revenues for online ads exceeded $150 billion USD in 2021 and continue to grow every year. Those ads are powered by online tracking, profiling, and targeting: a vast corporate surveillance network that harvests and analyzes our every click, query, and more. The average web page shares data with dozens of third parties. The average mobile app does the same, and many apps collect highly sensitive information like location, even when they’re not in use. Digital tracking also reaches into the physical world: shopping centers use automatic license-plate readers to track traffic through their parking lots; businesses, concert organizers, and political campaigns use Bluetooth and WiFi beacons to perform passive monitoring of people in their area; and retail stores use face recognition to identify customers, screen for theft, and deliver targeted ads. In order to target ads to users based on their online behaviors, the ad-tech industry relies on sophisticated tracking techniques that collect information about users as they browse the web and interact with apps on their mobile devices. Users are tagged with unique identifiers to pinpoint them and categorize their consumer behaviors into cohorts and micro-audiences: users from a certain neighborhood who are interested in used Audi cars, for instance. On mobile devices, advertisers can use an identifier provided by the phone itself, in the form of a unique advertising identifier. Advertisers also use cross-device tracking to link a users’ mobile devices to their workstation and other devices they may have in the home, forming an overall picture of their usage of all the different devices they interact with. Princeton researchers found that the vast majority of online tracking comes from large tech firms such as Google and Facebook, and is disproportionately on sites that rely heavily on advertising revenue like news and arts websites. How does tracking happen on the web? On the web, tracking is most often performed with cookies—specifically third-party cookies, which come from sites that the user doesn’t even directly visit, but are instead loaded by the “first-party” site the user is on. For instance, a news site may include an advertisement or interactive game from an advertiser server that is able to set cookies on a user’s browser. If this advertiser is loaded on multiple sites a user visits, the advertiser will be able to track that user across multiple sites via the cookie they set previously. One way to evade these trackers is to “clear cookies” on your browser. But this won’t always protect from other forms of web tracking. For example, browser fingerprinting uses different characteristics of a person’s browser (such as language, time zone, and fonts) that are not on their own unique, but when combined will uniquely identify a specific browser. What is Do Not Track? A number of initiatives have attempted to limit the scope of online tracking. Originally proposed in 2009, the Do Not Track (DNT) web header sent a signal from a user’s browser to all sites they visited stating that user’s preference not to be tracked. The DNT header initiative suffered from lackluster adoption by browsers and a lack of mechanisms to enforce the user's preference. To give the preference some teeth, EFF introduced our own DNT Policy, which sites could incorporate into their own privacy policies, and thus promise not to track browsers that opted out using the DNT header. In return, tracker blockers such as our own Privacy Badger would not block sites which abided by this policy. Eventually, DNT was superseded by individual browser initiatives to block or limit trackers, and was abandoned by standards bodies such as the W3C. What is Global Privacy Control? In 2020, a new specification titled Global Privacy Control (GPC) was introduced at the W3C. It picks up momentum where DNT began to lag. It also pairs with the newly passed California Consumer Privacy Act (CCPA) and the now well-known GDPR. At its core, it works like DNT: a user’s browser sends a distinct signal to websites it visits that invokes the GPC. But now, the signal is legally binding to companies in places with applicable privacy laws. In California, for example, it allows users to opt out of having their data shared or sold. This automated, “one and done” opt-out tool is far easier for people to use than manually opting-out, one at a time, at all the sites that a person visits. Tools such as Privacy Badger have incorporated this functionality, coupled with the DNT controls that are already in place. This new specification is tailored to requirements of new laws like CCPA. However, it doesn’t completely protect users from the dangerous advertising and tracking industry, given gaps in current law. How do I prevent myself from being tracked? EFF’s own Privacy Badger is a browser extension that automatically learns to block invisible trackers. It identifies the third-party resources you encounter across the web, determines which are trackers, and prevents those trackers from being loaded. In this way, it allows the useful third-party resources that are needed to display a webpage and help it function properly, while blocking the unnecessary and invasive trackers. Other browser extensions such as uBlock Origin and Disconnect can be used in combination with Privacy Badger to provide a defense in-depth approach, layering different protections against trackers in your browser. Browsers themselves have introduced various protections against web tracking as well. Apple’s Safari browser and iOS use Intelligent Tracking Prevention to protect against the latest tracking technologies. Enhanced Tracking Protection in Firefox uses the Disconnect block list as well as a number of Mozilla’s own techniques to block trackers. Brave is a privacy-focused browser employing a unique set of protections of its own, including protection against browser fingerprinting technologies. Users who wish to protect themselves against not only online trackers but also more advanced threats may consider Tor Browser, which puts an anonymous web-browsing experience above all else. Note: this is an updated page. View the original archived version.
- ✓ og:image — preview
- ✓ twitter:card — summary
📐 How the SEO Pillar score is calculated
SEO Pillar = Title (20 pts) + Meta Desc (20 pts) + Heading Hierarchy (20 pts) + Indexability (20 pts) + Social/OG (20 pts)
Each sub-score is derived from the checks above. Canonical tag, lang attribute, og:image, and a single H1 are the highest-impact items.
AI Readiness / GEO
81/100 40 % of Global Score 🟢 High ConfidenceThis pillar aggregates citation share, hallucination risk, bot access, schema health, and content extractability. The individual diagnostic sections below contribute to this score.
Is AI lying about your brand? This panel measures how likely LLMs are to hallucinate facts when extracting information from your page.
🤖 Bot Access Matrix
📊 Structure & Information Density Docs
🏷️ Schema Health Docs
Schema Coverage Map
📐 AI Efficiency Metrics Docs
Token Bloat Research
Multimodal Readiness
TDM Rights
🔥 Structural Entropy Check Research
🔬 AI-Crawler Simulation
See your website the way AI crawlers do. CSS stripped, structure labeled, content chunked.
Toggle to "AI Agent View" to see what GPTBot, ClaudeBot, and other AI crawlers actually extract from this page.
AI Answer Preview
NEWSee how AI models summarize your site. Left: your actual content. Right: what the LLM extracts and says about you.
The LLM Interpretation
AI-VERIFIEDSEODiff AI analyzed the extracted content of donottrack.us and produced this structured business intelligence. Fields marked SEMANTIC VOID indicate information the AI could not find — a critical gap in your site’s machine-readability.
🔧 Tech Stack
Performance & Speed
62/100 20 % of Global Score 🟢 High Confidence⏱️ Time to First Byte
Google considers <200 ms "good". AI crawlers may have even shorter timeouts.
📦 Page Weight
DOM nodes
HTML payload
🗄️ Cache & CDN
- ✓ Cache-Control header →
public, max-age=1800 - ✗ CDN cache status
- ✗ CDN detected
🔬 Tracker Tax
tracker scripts
third-party domains
token overhead
📐 How the Performance Pillar score is calculated
Perf Pillar = TTFB (35 pts) + Page Weight (25 pts) + Cache/CDN (20 pts) + Tracker Tax (20 pts)
TTFB <200 ms = full marks. DOM >3000 or payload >300 KB incurs heavy penalties. Tracker scripts beyond 5 reduce score.
Architecture & Trust
53/100 15 % of Global Score 🟡 Medium Confidence🗺️ Sitemap & Robots
- ✗ Sitemap declared in robots.txt
- ✓ Googlebot allowed
- ✓ GPTBot allowed
- ✓ ClaudeBot allowed
🔗 Linking
internal links
external links
🔒 Security & Trust
- ✗ HSTS header (Strict-Transport-Security)
- ✗ Content-Security-Policy header
- ✓ HTTP status 200 OK (got 200)
♿ Accessibility Signals
- ✓ HTML lang attribute → en
- ✓ Meta viewport for mobile
- ✗ Single H1 for screen readers
📐 How the Architecture Pillar score is calculated
Arch Pillar = Sitemap & Robots (30 pts) + Linking (25 pts) + Security (25 pts) + Accessibility (20 pts)
Having a valid sitemap, allowing AI bots, HSTS, and a good internal link count are the highest-impact items.
🏅 AI-Verified Trust Badge
Your site scores 59/100. Reach 80+ to unlock the green "AI-Verified" badge. Fix the issues below to improve your score.
<a href="https://seodiff.io/radar/domains/donottrack.us" rel="noopener"><img src="https://seodiff.io/api/v1/badge?domain=donottrack.us" alt="AI-Verified by SEODiff" width="280" height="52"></a>
💡 Paste in your site footer, GitHub README, or email signature. Badge updates automatically as your score changes.
🔗 Similar Sites
Domains with a similar tech stack, industry, and AI readiness profile to donottrack.us. Compare side-by-side.
| Domain | ACRI | AI Score | Tech Stack | Token Bloat | Schema | |
|---|---|---|---|---|---|---|
| donottrack.us (this site) | 59 | 74 | Drupal | 5.2× | 0 | — |
| vegansociety.com | 69 | 80 | Drupal | 6.3× | 0 | Compare → |
| fra.europa.eu | 69 | 78 | Drupal | 3.0× | 0 | Compare → |
| sgu.ru | 69 | 79 | Drupal | 2.7× | 0 | Compare → |
| sascha.ru | 69 | 78 | Custom / Proprietary | 6.1× | 0 | Compare → |
| winemeridian.com | 69 | 82 | WordPress | 5.2× | 1 | Compare → |
📊 Semantic Share of Voice
How often would an AI cite donottrack.us when users ask about topics in this domain's niche? We run entity queries through our 188k-page search index and measure citation probability.
Analyzing citation landscape…
Remediation Patches
COPY-PASTEAuto-generated code fixes tailored to donottrack.us. Copy and paste these into your codebase to improve AI visibility. These patches are mathematically proven to increase extraction accuracy →
<script type="application/ld+json">
{
"@context": "https://schema.org",
"@type": "Organization",
"name": "Donottrack",
"url": "https://donottrack.us",
"logo": "https://www.eff.org/sites/all/themes/frontier/favicon.ico",
"sameAs": []
}
</script>
<script type="application/ld+json">
{
"@context": "https://schema.org",
"@type": "WebSite",
"name": "Donottrack",
"url": "https://donottrack.us",
"potentialAction": {
"@type": "SearchAction",
"target": "https://donottrack.us/search?q={search_term_string}",
"query-input": "required name=search_term_string"
}
}
</script>
<script type="application/ld+json">
{
"@context": "https://schema.org",
"@type": "FAQPage",
"mainEntity": [
{
"@type": "Question",
"name": "What is Donottrack?",
"acceptedAnswer": {
"@type": "Answer",
"text": "Add your answer here — describe what Donottrack does in 1-2 sentences."
}
},
{
"@type": "Question",
"name": "How does Donottrack work?",
"acceptedAnswer": {
"@type": "Answer",
"text": "Explain the key features and how users interact with Donottrack."
}
}
]
}
</script>
Projected Impact
ROI EST.If you apply the patches above, here's the estimated improvement for donottrack.us:
*Estimates based on SEODiff's scoring model. Actual results depend on implementation quality.
📋 Data Export
Download scores and metadata for audits, client reports, or CI/CD pipelines. Exports contain computed metrics only (no copyrighted content).
All data is generated automatically and updated with each crawl. JSON exports contain scores and metadata only (no copyrighted content).
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