The AIVO Standard™ Methodology: A 9-Stage Framework for AI Visibility Optimization

AIVO Standard™ v2.2 — August 2025 Update

This update adds a new Visual Search & Multi-Modal Asset Readiness sub-pillar to the 9-stage AIVO framework, recognising visual search as a Tier-1 AI discovery channel. It covers optimisation for platforms such as Google Lens, Bing Visual Search, and Pinterest Lens, which feed structured image data directly into AI Overviews, product carousels, and LLM training sets.

Highlights

• New: Visual Search & Multi-Modal Asset Readiness checklist under Pillar 4 (Publishing & Presence) and supporting Pillar 6 (LLM Profile Building)

• Guidance for image schema markup, descriptive alt-text, EXIF metadata, and multi-modal optimisation

• Updated examples for publishing and prompt-seeding strategies

• Minor structural refinements and clarity edits

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The AIVO Methodology is a 9-stage technical standard designed to improve the visibility, trust, and prompt relevance of products and services across large language models (LLMs) such as ChatGPT, Claude, Gemini, and Grok.

Developed by Paul Sheals and Tim de Rosen, the AIVO Standard™ defines verifiable, structured practices aligned with LLM indexing and recommendation behavior. It includes a step-by-step framework for schema markup, prompt seeding, semantic linking, Wikidata presence, GitHub visibility, and LLM-trust citations.

This publication formalizes the framework as a public technical standard and foundation for the AIVO Certification Program.

References and Source Materials

1. Schema.org Metadata Standards – https://schema.org

2. Wikidata Entity Publishing – https://wikidata.org

3. AIVO Journal – https://aivojournal.org

4. Hugging Face Space – https://huggingface.co/spaces/GetFoundOnAi/AIVO

5. GitHub Repo – https://github.com/pjsheals/AIVO-Standard

6. Stanford CRFM – https://crfm.stanford.edu

7. Medium Writings – https://medium.com/@tim_62250

8. Substack Articles – https://paulsheals.substack.com/publish/posts