SEO / GEO

AI discovery needs more than a sitemap

What Kyanite adds so search engines and AI assistants can understand the tools, products, proof, and support path.

AI discovery works when a site gives crawlers and answer engines structured, quotable, current facts about what exists, who it helps, and what proof supports it. A sitemap is necessary. It is not enough.

Generative Engine Optimization is mostly discipline. Say the answer early. Use real names. Add structured data. Keep public proof current. Make the commercial next step obvious.

The AI-readable stack

  • /sitemap.xml for canonical crawl coverage
  • /llms.txt for answer-engine context
  • /ai-sitemap.json for structured products, repos, and posts
  • JSON-LD for Organization, WebSite, Article, Service, Product, and FAQ entities
  • Direct-answer paragraphs at the top of pages and posts

The tradeoff is maintenance. These files cannot be aspirational. If the repo list changes, the proof layer needs to change with it.

GEO is strongest when it is useful to humans too

Answer engines and human readers both reward the same thing: specific claims with clear evidence. "We build AI tools" is weak. "We build MCP servers, video automation, localization QA, repo diagnostics, and open-source tools backed by public KyaniteLabs repositories" is stronger because it can be checked.

FAQ

What is GEO?

GEO, or Generative Engine Optimization, is structuring web content so AI answer engines can accurately summarize, cite, and route users to it.

Work with Kyanite

Want this working in your environment?

If this post describes a Kyanite tool or result you need, implementation help can cover setup, advising, docs, examples, checks, and a usable handoff.

Fit boundary

Kyanite offers help grounded in its tools, products, and build practice. Broader consulting routes through PuenteWorks.

Keep following the system.

Agents need verifiable tools, not better prompt theater

The useful agent pattern is not a prettier prompt. It is a tool surface the agent can call, inspect, verify, and revise.

Repo history is a product signal

A repo is not just storage. It is evidence of decisions, repairs, release behavior, naming drift, test gaps, and what the builder actually knows how to finish.

Implementation help is part of the product surface

A useful open-source tool still needs a path from public repo to working environment. That path is product work, not an afterthought.

Why mcp-video matters

mcp-video is a video editing MCP server that gives AI agents direct handles on timelines, effects, FFmpeg, and finished media.

Infinite monkeys, LLMs, and the room around the machine

The argument behind the video: output quality is not just probability. It is architecture, filters, and human taste.

What a working AI tool needs before people can use it

A practical checklist for turning a working tool, workflow, or rough app into something other people can understand, install, and use.

MCP server implementation checklist

The checklist Kyanite uses to decide whether an MCP server is a toy, a usable tool, or something worth implementing.

Repo archaeology turns history into proof

Why commit history is one of the strongest proof sources for learning diagnostics, implementation help, and engineering trust.