𝗧𝗵𝗲 𝗢𝗽𝗲𝗻-𝗦𝗼𝘂𝗿𝗰𝗲 𝗔𝗜 𝗦𝗲𝗮𝗿𝗰𝗵 𝗔𝘂𝗱𝗶𝘁 𝗦𝘁𝗮𝗰𝗸
Most AI Search work is too vague. Teams ask how to show up in ChatGPT. They jump to prompts and dashboards. They ignore the page layer.
If the page layer is messy, the system is hard to read. A low citation rate is often a crawl problem. It is often a schema problem. It is often an entity problem.
I build geo-audit. It is an open-source layer for AI Search visibility. The first part is boring. It crawls the site. It inspects head tags. It parses JSON-LD. It checks canonical URLs.
Deterministic checks remove noise. Do not ask an LLM if a page is good. Ask these questions first:
- Does the route fetch?
- Is the final URL stable?
- Is there one clear title?
- Is there a meta description?
- Does the canonical URL match the source?
- Is there a visible H1?
- Does JSON-LD exist?
- Does JSON-LD parse?
These checks do not need a model. The base layer should be code. It must be repeatable. It must be boring.
Two new modules help:
- site-crawl-lite: Provides a route inventory.
- head-schema-gate: Checks the homepage and target routes.
The stack has layers:
- Layer 1: Technical baseline.
- Layer 2: Publishing readiness.
- Layer 3: Authority checks.
- Layer 4: Business measurement.
geo-audit handles the first layer. It is agent-friendly. It keeps secrets local. It produces proof artifacts.
I ran these gates on my own site. site-crawl-lite scored 99/100. head-schema-gate scored 94/100. This signal is useful. It shows I need a schema pass. I do not need a rewrite.
Source: https://dev.to/gshevchenko/the-open-source-ai-search-visibility-audit-stack-im-building-1i1p Optional learning community: https://t.me/GyaanSetuAi