AI-Ready Websites in the Philippines
A live directory of homegrown Philippine websites that have achieved a perfect score on our 20-point AI readiness audit. We scan the web for technical barriers that prevent AI systems from reading and citing local content.
Methodology
Automated Scanning via SnifferBot. The data below is generated by SnifferBot, our proprietary headless crawler. It periodically scans a verified seed list of Philippine business, government, and media domains. We do not rely on user submissions for the directory table.
The 20-Point Audit. For each URL, SnifferBot runs the same deterministic tests used in the manual auditor: HTTPS, TTFB, robots.txt syntax, sitemap validation, semantic HTML, schema markup, content ratio, and the four AI discovery files. The same 20 checks and the same deduction table are published on the live Ask W Auditor at wholesaledito.store/ask-w/website-ai-readiness-auditor.
Perfect Scores Only. The directory table only lists websites that pass all 20 checks (a score of 100/100). Sites scoring 99 or below are excluded from the public list to maintain a strict standard of technical readiness.
Zero AI in the Execution Path. SnifferBot uses no generative AI to evaluate the sites. It uses cURL requests, regex matching, and DOM parsing. The logic is deterministic and publicly reproducible.
Bot Verification. Webmasters can verify SnifferBot traffic by checking their server logs for the User-Agent: SnifferBot/1.0 (+https://www.clickerwaynezelle.com/research/ai-ready-websites-in-the-philippines).
Disclosure. This directory is published by Clickerwayne Zelle Solutions Inc, which also operates Wholesale Dito Store. Neither domain is listed in the perfect-score table, to keep the table free of self-scoring. Their live audit history is public at wholesaledito.store/ask-w/website-ai-readiness-auditor and anyone can verify it.
Audit Results
The table below lists only websites that passed all 20 checks. If you run a test on the right and score 100, your site will appear here. Readiness Rate is the share of scanned sites that pass all 20 checks of this specific tool. It is not a measure of AI visibility. The live auditor's own FAQ states that passing does not guarantee citation, ranking, or traffic. The public Ask W Auditor history shows the operator's own domain at 100/100, which confirms the tool can return a perfect score and that the 0.0% figure reflects this particular seed list, not a limitation of the tool.
| Website | Score | TTFB | Last Scanned |
|---|---|---|---|
| No websites in the seed list have achieved a perfect score yet. The public Ask W Auditor history does show one domain at 100/100, which is not part of this seed list. | |||
What This Tool Checks
The auditor runs twenty checks. Each one is a specific technical condition that affects whether an AI system can read and cite the target page. The discovery-file checks at the bottom are proposed or experimental formats, and the page labels them as such.
- Secure TLS/HTTPS Infrastructure. Confirms the URL is served over HTTPS.
- Time-To-First-Byte (TTFB). Measures how quickly the server begins to respond. Values above 1.5 seconds are flagged.
- Multi-Bot Access Simulation. Reads
robots.txtand reports access for twelve AI crawlers grouped by training, search-index, and user-retrieval roles. - robots.txt syntax. Scans for unknown directives and missing colon separators.
- Sitemap presence. Checks
/sitemap.xmland/sitemap_index.xmland confirms the XML shape. - Mobile viewport. Checks for
<meta name="viewport">. - html lang. Checks for a
langattribute on the<html>tag. - Core semantic elements. Checks for a
<title>, meta description, and primary<h1>. - HTML5 landmarks. Checks for
<main>,<section>,<article>,<nav>, and<aside>. - Canonical tag. Confirms a self-referencing or authoritative URL is declared.
- Open Graph. Checks for
og:titleand related tags. - Image alt text. Counts images without alt attributes.
- Sequential heading hierarchy. Confirms that heading levels do not skip.
- JSON-LD entity schema. Parses all JSON-LD blocks and reports detected
@typevalues. - Schema required properties. Confirms required properties per type are present.
- Content chunk size. Flags paragraphs over 150 words.
- Content-to-HTML ratio. Compares visible text to total markup. Below 10% is flagged.
- Last-Modified header. Confirms the server sends a freshness signal.
- OKF bundle (experimental). Checks for an Open Knowledge Format bundle at
/.okf/index.md. This is a Google Cloud format for internal knowledge, not a web discovery signal. - AI discovery files suite (emerging and experimental). Checks for
llms.txt,llms-full.txt,/.well-known/ai.txt, and/.well-known/ard.json. Google's own documentation states Google Search does not use llms.txt. ai.txt is an IETF draft. ard.json is an August 2026 spec for publishers of callable APIs.
Deduction Table
The table below is copied from the live Ask W Auditor. Each check carries a fixed deduction. The score is 100 minus the sum of deductions, floored at zero. Anyone can reproduce a score by hand from this table.
| Check | Deduction |
|---|---|
| HTTPS | 10 |
| Time-To-First-Byte (TTFB) | 5 |
| robots.txt syntax | 5 |
| Sitemap | 5 |
| Mobile viewport | 5 |
| html lang | 3 |
| Core semantic elements | 10 |
| HTML5 landmarks | 5 |
| Canonical tag | 5 |
| Open Graph | 5 |
| Image alt text | 5 |
| Heading hierarchy | 5 |
| Entity schema missing | 15 |
| Schema required properties | 5 |
| Oversized content chunks | 5 |
| Content-to-HTML ratio | 3 |
| Last-Modified header | 2 |
| OKF bundle | 2 |
| Each missing discovery file (llms.txt, llms-full.txt, ai.txt, ard.json) | 5 each |
The sum of all deductions is 122. A site that fails every check would score minus 22 before the floor is applied, so the result is shown as zero. This is the same arithmetic used by the live auditor. The floor is applied after summation, not before. The overshoot is documented here so anyone adding the column gets the same answer the tool does.
How to Use This Tool
The auditor is a pre-publish checklist. Run it on a page before you publish, or on a whole site after a rebuild, and fix the failures it reports. We use it that way ourselves. We run every new page on our own sites through this audit before it goes live.
Most of the twenty checks are technical SEO and page-quality conditions that affect both traditional search and AI crawlers: HTTPS, Time-To-First-Byte, robots.txt syntax, sitemap validity, mobile viewport, html lang, title and meta description and H1, HTML5 landmarks, canonical tag, Open Graph tags, image alt text, heading order, JSON-LD schema, content chunk size, content-to-HTML ratio, and the Last-Modified header. If you are building a new page or a new site, these are the things to get right before launch, not after.
The four discovery files at the end of the list are a different matter. They are proposed or experimental formats. Google's own documentation states that Google Search does not use llms.txt. We check them because they are part of the emerging AI discovery landscape and because the live auditor checks them, not because they are required for ranking or citation today.
Passing the audit means the page has cleared the technical conditions on this list. It does not guarantee ranking, citation, or traffic. Those depend on content quality, authority, and links from others. The audit removes barriers. It does not create demand.
Definitions
What is TTFB?
Time-To-First-Byte is the interval between when a client sends an HTTP request and when the server begins to send the first byte of the response. It includes DNS resolution, TCP connection, TLS handshake, and server processing time. A TTFB above 1.5 seconds suggests that fast automated agents may time out.
What is llms.txt?
llms.txt is a plain-text file at the domain root that gives large language models a curated map of a site's most important pages. It is a Markdown document with an H1 heading, a blockquote summary, and lists of links. It is a discovery aid for AI systems, not a search ranking factor. Google's own documentation states that Google Search does not use llms.txt.
What is llms-full.txt?
llms-full.txt is the companion to llms.txt. Where llms.txt is a map, llms-full.txt contains the actual body text of the site's key pages so that a retrieval-augmented generation pipeline can ingest everything in one fetch.
What is ai.txt?
ai.txt is a machine-readable usage policy for AI systems. It declares training, scraping, indexing, and caching permissions, path-level allow and deny rules, and per-agent overrides. The format is defined by the IETF Internet-Draft draft-car-ai-txt-wellknown-00. The draft is an individual submission, has not been adopted by a working group, and expires December 14, 2026 unless renewed.
What is ard.json?
ard.json is the manifest used by the Agentic Resource Discovery specification. It is published at /.well-known/ard.json and lists the AI-callable resources a domain offers, such as MCP servers, A2A agents, skills, APIs, and nested catalogs. The predecessor path is /.well-known/ai-catalog.json. The spec is for publishers of callable resources. A content site has no action to take today.
What is the OKF bundle?
The Open Knowledge Format is a directory of Markdown files with YAML frontmatter, each describing a single concept. It is a newer discovery path that sits alongside llms.txt. The canonical location is /.okf/index.md. It is a Google Cloud format for internal organizational knowledge, not a web discovery signal, and most sites do not have it.
What is a JSON-LD entity schema?
A JSON-LD entity schema is a structured data block that tells AI systems what an entity on a page is. Common types are Organization, LocalBusiness, WebSite, FAQPage, and WebApplication. AI systems and search engines use these blocks to categorize and cite content.
What is the multi-bot access matrix?
The matrix simulates whether each of twelve AI crawlers is permitted to fetch the target site. Ten are fully checked against robots.txt. Two (Google-Extended and Applebot-Extended) are robots.txt tokens only, not live crawler user-agents, and are reported accordingly.
What is a sequential heading hierarchy?
A sequential heading hierarchy means that heading levels do not skip. A page that goes from an H1 straight to an H3 is flagged because the gap leaves ambiguity about the relationship between the sections. AI systems use headings as structural clues about content.
Frequently Asked Questions
What is an AI readiness audit?
An AI readiness audit checks whether a website can be found, read, and cited by AI systems. It verifies twenty technical conditions covering infrastructure, semantic HTML, structured data, content structure, and AI discovery files.
What files does the auditor check for?
The auditor checks for five discovery paths: llms.txt at the domain root, llms-full.txt at the domain root, ai.txt at /.well-known/ai.txt, ard.json at /.well-known/ard.json, and an OKF bundle at /.okf/index.md. The OKF bundle is optional and most sites do not yet have it. The discovery files are proposed or experimental formats, and Google's own documentation states Google Search does not use llms.txt.
Does this auditor use AI?
No. The auditor is deterministic. Every check is a specific HTTP request, regex match, or DOM query. There is no AI, no inference, and no interpretation. The score is the sum of specific checks.
Why does TTFB matter for AI crawlers?
AI crawlers often have tighter timeouts than traditional search crawlers. If Time-To-First-Byte exceeds 1.5 seconds, faster crawlers may time out before the page begins to render. TTFB below 0.8 seconds is optimal.
What is the multi-bot access matrix?
The matrix simulates whether twelve AI crawlers are allowed to fetch the target site according to its robots.txt. Crawlers are grouped by role: Training (model training), Search-index (AI search answers), and User-retrieval (live user-triggered fetches).
Does passing this audit guarantee AI visibility?
No. Passing the audit means the site has removed technical barriers that would prevent AI systems from reading it. It does not guarantee that AI systems will cite the site, rank it, or send traffic to it. AI visibility depends on content authority, structure, and citation by others.
Is this directory the same as the Ask W Auditor?
Yes. The twenty checks, the deduction table, and the scoring are the same. This directory mirrors the results that the live Ask W Auditor publishes at wholesaledito.store/ask-w/website-ai-readiness-auditor. The two sites are operated by the same company.
Why are wholesaledito.store and clickerwaynezelle.com not in the perfect-score table?
Both domains are operated by Clickerwayne Zelle Solutions Inc, the publisher of this directory. Listing them in the perfect-score table would be self-scoring. Their live audit history is public at the Ask W Auditor, and anyone can verify it there. This is a disclosure, not a claim of neutrality.
Is the auditor safe to run on any URL?
The auditor blocks private, loopback, link-local, and cloud metadata address ranges before any outbound request. It also rate-limits scans to one per minute and ten per hour per session. These protections prevent the tool from being used to probe internal infrastructure.