# Case studies · Onto
> Industry teardowns of how major brands score for AI agents. Live AIO scans, specific bottlenecks, and the exact infrastructure fix.

**Source:** /case-studies
**Extracted:** 2026-05-20T20:59:17.519Z

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Industry teardowns

## How major brands are _invisible to AI_ right now.

Deep technical tear-downs of how Fortune 500 companies, major SaaS platforms, and well-known developer tools are actively sabotaging their visibility in AI search, chatbots, and autonomous shopping agents. Every teardown includes the live AIO score, the specific bottleneck causing the failure, and the exact infrastructure fix. **We test in production. We don't make this up.**

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All teardowns · 3 · live AIO scans

*   [
    
    LinearMay 20267 min read
    
    ## Linear's pricing page is 1.8 MB of JavaScript for a 0.3 KB page.
    
    Linear is widely admired by developers for product polish. Their pricing page tells the opposite story to AI agents: 1.8 MB of JS for what reduces to 0.3 KB of actual content. 462,336 tokens of garbage. A single GPT-4 query against this URL costs $4.62 — and the agent comes back with a high hallucination risk.
    
    State
    
    20 / 100
    
    Payload
    
    1.8 MB → 0.3 KB
    
    Bottleneck
    
    96.6% React Tax · no JSON-LD
    
    Read teardown
    
    ](/case-studies/linear-pricing)
*   [
    
    StripeMay 20266 min read
    
    ## Stripe's pricing page ships 690 KB of noise for a 0.8 KB story.
    
    Stripe passes every structural check the AIO scanner runs — JSON-LD present, heading hierarchy strong, robots.txt allowed. But it serves AI agents the same 690 KB React payload it serves humans. The result: 849× more bytes than the agent actually needs, and a $1.76 GPT-4 call for a page that should cost $0.002.
    
    State
    
    65 / 100
    
    Payload
    
    690 KB → 0.8 KB
    
    Bottleneck
    
    Content negotiation missing
    
    Read teardown
    
    ](/case-studies/stripe-pricing)
*   [
    
    ResendMay 20265 min read
    
    ## Resend already serves AI-ready Markdown. Here's what 90/100 looks like.
    
    Resend is the rare SaaS company that already speaks Markdown to AI agents. Native content negotiation, JSON-LD schema present, robots open, 0.9% React Tax. Every check passes. The remaining 10 points are about observability and policy — not infrastructure. This is what agent-ready actually looks like.
    
    State
    
    90 / 100
    
    Payload
    
    453 KB → 1.0 KB
    
    Bottleneck
    
    All 5 checks pass
    
    Read teardown
    
    ](/case-studies/resend-pricing)

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