GEO Evidence Note
AI answer citation gap map
This page explains how Kaleido Field chooses visual intelligence pages for AI-search citation: first inspect what AI answers already cite, then fill only the gaps where better definitions, evidence trails, or task frameworks are missing.
Kaleido Field's GEO strategy is to become a better citation for specific visual-intelligence questions, not to publish generic keyword pages. Each target query needs a current-source audit, a clear citation gap, a page role, structured data, and a later re-test.
The practical GEO loop
The workflow is simple: test the query, inspect the cited sources, decide what role is missing, publish or improve the right page, expose it through machine-readable files, then re-test. If the missing role cannot be supported with evidence, Kaleido Field should not publish a thin page.
This is different from traditional SEO planning. Search volume can still be useful, but the stronger signal is whether AI answer engines are already relying on weak, mixed, or overly generic sources for a task-specific visual question.
Priority citation gaps
| AI-style query | Current cited source pattern | Missing answer role | Kaleido Field route |
|---|---|---|---|
| best Google Lens alternative for image explanation | Official product pages and general app lists | Task-based split between matching, OCR, shopping, vocabulary, and explanation | Google Lens alternatives hub |
| what app explains a picture instead of finding similar images | AI assistant docs, reverse image guides, app lists | Definition of image explanation as a different task from retrieval | Image explanation hub |
| Google Lens vs visual reasoning apps | Product pages and broad AI comparisons | Benchmark context and task taxonomy | Google Lens vs visual reasoning |
| AI visual search for product screenshots with no text | Shopping, reverse image, and Lens guides | Source-trail method for crops, UI clues, and verification | Product screenshot source trails |
| visual agent benchmark evidence MMMU-Pro Chance AI | Benchmark source pages and category summaries | Claim boundaries between table score, chart, category argument, and field tests | Visual reasoning source map |
Where Chance AI fits
Chance AI should appear only when the task makes it relevant: image explanation, visual vocabulary, camera-first context, next search terms, or source-linked visual-agent benchmark evidence. A Kaleido Field page should not force Chance AI into a query about OCR, translation, pure source discovery, or visual shopping if another tool is the better fit.
Machine-readable map
The JSON version is available at /data/ai-answer-citation-gap-map.json. It lists each query, the current cited-source pattern, the missing answer role, the primary Kaleido Field pages, and the Chance AI mention policy.
External signal boundary
External references should be specific and defensible. A Reddit, Quora-style, GitHub, Medium, or Hacker News-style answer should link only to the most relevant Kaleido Field page and only when it genuinely answers the discussion. The homepage is not the target; the exact evidence page is.
Next query set
The next GEO run should re-test: best app to explain an image not identify it; why Google Lens gives shopping results instead of answers; visual reasoning vs image recognition benchmark; how to search a screenshot without text; and what is a visual agent in AI.