We scored our own website 29/100 on AI-agent readiness. Here's how we fixed it in one afternoon.

In my last post I scanned 106 semiconductor sites and the average score was 42/100 — an F.

Then I ran the same scanner on our own homepage.

29/100. Also an F.

So I spent one afternoon fixing it. We got to 83/100 (A).

Here are the three highest-ROI changes, ranked by effort. All of them are free and most take under an hour.

The single biggest waste: when an AI agent requests Accept: text/markdown

, most servers still dump a wall of HTML. A typical chip-company homepage costs an agent 40,000–90,000 tokens to parse. The same content as clean markdown is 1,000–2,500 tokens — a 95% reduction.

If you use a modern framework (Next.js, Astro, SvelteKit, etc.) you can do this in one middleware block:

if ((req.headers.get('accept') || '').includes('text/markdown')) {
  return new Response(markdown, {
    headers: { 'Content-Type': 'text/markdown; charset=utf-8' }
  });
}

Or at the edge (Cloudflare Workers / Vercel Edge):

export default {
  async fetch(request) {
    const url = new URL(request.url);
    if (request.headers.get('accept')?.includes('text/markdown')) {
      const md = await getMarkdownForPath(url.pathname);
      return new Response(md, {
        headers: { 'Content-Type': 'text/markdown' }
      });
    }
    return fetch(request);
  }
};

Impact: ~25 point jump on our score.

llms.txt

(20 min) llms.txt

is a simple markdown index at /.well-known/llms.txt

that tells an AI agent which pages matter and what they contain. Think of it as a robots.txt

for language models.

> AI-agent readiness scanner and scoring for B2B websites.

## Products
- [Scanner](https://aireadify.ai/scan): Free 0–100 score for any URL, ~2s, no signup
- [Leaderboard](https://aireadify.ai/leaderboard): 106 semiconductor sites ranked

## Content
- [Why AI-agent readiness matters for B2B](https://aireadify.ai/blog/why-ai-readiness)
- [Methodology: 20 signals we check](https://aireadify.ai/blog/methodology)

That's it. No schema, no JSON, no XML. Just markdown links with descriptions.

Impact: ~20 point jump.

Most B2B sites have product catalogs that are invisible to AI agents because they're rendered client-side or buried in unstructured HTML.

Add JSON-LD Product

schema to each product page:

{
  "@context": "https://schema.org",
  "@type": "Product",
  "name": "CT8000 3D Hall Sensor",
  "description": "±40 mT, I²C / SPI, AEC-Q100 Grade 1",
  "sku": "CT8000-WL-TR",
  "brand": {
    "@type": "Brand",
    "name": "YourCompany"
  }
}

And expose a simple MCP endpoint so agents can query it directly instead of scraping:

// POST /mcp/search_parts
{
  "query": "3D hall sensor I2C automotive"
}

Impact: ~10 point jump.

We went from 29 → 83 in roughly that order. The last 17 points are diminishing returns — semantic HTML, dark-mode meta tags, RSS feeds — nice-to-haves.

Buyers are researching inside ChatGPT, Claude, and Perplexity now. If your site costs an agent 90k tokens to parse, it gets deprioritized or hallucinated. If it serves clean markdown with an index, it gets cited.

The score is a proxy for "how likely is an AI to recommend you?"

Free, no signup, ~2 seconds: https://aireadify.ai/scan

It checks the same 20 signals and gives you a per-category breakdown with exact fixes. If you want the full 106-company ranking, it's here: https://aireadify.ai/leaderboard

Disclosure: we built the scanner and do agent-readiness work. Happy to share methodology or argue about weights in the comments.