---
title: "Making a Next.js and Convex site AI-readable"
description: "Original SYMBaiEX implementation notes on HTML, Markdown, crawler policy, structured data, IndexNow, and performance evidence."
canonical: "https://www.symbaiex.com/research/ai-readable-web"
---
# Making a Next.js and Convex site AI-readable

> The reliable way to make a site discoverable to people, search engines, and user-directed agents is to publish one truthful content graph in several useful representations.

## The implementation stack

HTML remains the human-readable authority. Markdown negotiation lowers retrieval friction. llms.txt provides a bounded curated map. Robots and sitemaps define crawl boundaries. Feeds and IndexNow communicate freshness. JSON-LD explains stable entities. Safe logs reveal real crawler behavior.

## Performance benchmark

A production Lighthouse trace measured about 2,578 KiB on the home route before the performance pass; a 2.05 MiB decorative sprite was the largest request. Responsive derivatives, removal of global idle prefetching, and deferred decorative media reduced a later trace to about 489 KiB. Lab scores vary with cache state and browser scheduling, so payload and LCP trends are the durable acceptance signal.

## Authority rules

Answer first, show method and caveats, date the work, cite primary sources, keep structured data aligned with visible text, separate AI-search visibility from training permission, and disclose AI authorship where it matters.

## References

- https://developers.google.com/search/docs/appearance/ai-features
- https://developers.google.com/search/docs/fundamentals/ai-optimization-guide
- https://developers.google.com/search/docs/appearance/structured-data/sd-policies
- https://www.indexnow.org/documentation
- https://blogs.bing.com/webmaster/February-2026/Introducing-AI-Performance-in-Bing-Webmaster-Tools-Public-Preview