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AIVO Standard v2.2: A Canonical Framework for Multi-Modal AI Visibility

Editorial Board

• 5 min read
AIVO Standard v2.2: A Canonical Framework for Multi-Modal AI Visibility
AIVO Standard™ - Canonical Framework for Multi-Modal AI Visibility

Author: AIVO Standard Research Group
Journal: AIVO Journal
Version: 12 August 2025
Correspondence: support@aivostandard.org

Abstract

Large language models now combine text, image, and video to generate recommendations and answers. Traditional SEO, which targets page rank and keyword signals, does not cover the visibility determinants that drive assistant outputs across GPT-class models, Gemini, and Copilot. This article codifies AIVO Standard v2.2, the first version of the methodology that formalises multi-modal AI search, adding image and video asset readiness, visual indexing submissions, and cross-LLM metadata parity. We define evaluable constructs, propose auditable metrics, and provide certification criteria that map structured data, mention graphs, prompt discoverability, publishing channels, ecosystem profiles, trust signals, and continuous monitoring to multi-modal outcomes. The v2.2 additions include Stage 5.11 Visual Search and Multi-Modal Asset Readiness and a Stage 6 cross-reference that requires proactive submission of optimised visual assets to discovery engines.

1. Introduction

Search behaviour has shifted from blue links to assistant answers. Models ingest and retrieve from structured data, entity graphs, open repositories, and increasingly from visual inputs. A brand that fails to align its text and visual surfaces with assistant ingestion logic risks exclusion from AI recommendations regardless of its traditional rankings. AIVO Standardprovides a nine-stage, certifiable process for AI visibility across text and multi-modal surfaces. Version v2.2 makes visual search a first-class citizen within this process.

Contributions

  1. A formal multi-modal extension to the AIVO lifecycle that operationalises image and video readiness.
  2. A set of auditable metrics for assistant-layer visibility, including visual asset readiness and cross-LLM parity.
  3. Certification-grade checklists and proofs that align with open data ecosystems and assistant ingestion pathways.

Assistant outputs are seeded by upstream signals that differ from classic SEO. The AIVO Standard has documented nine stages covering objectives, foundation, mention graphs, prompt discoverability, AI-indexed publishing, indexing submissions, ecosystem profiles, trust signals, and continuous monitoring. Prior versions focused on text-centric signals. Version v2.1 introduced GPT-5 readiness for persistent retrieval presence, context clustering, citation disambiguation, and cross-LLM metadata parity. Version v2.2 extends this to visual search and multi-modal asset optimisation.

3. Methodological Overview

AIVO comprises nine sequential yet iterative stages. The multi-modal upgrade touches Stage 5 and Stage 6, and influences verification in Stages 8–9.

  • Stage 1: Define target discovery prompts and align with business outcomes.
  • Stage 2: Establish foundational presence with schema.org JSON-LD, Wikidata, and consistent entity metadata across properties.
  • Stage 3: Expand knowledge and mention graphs via Crunchbase, G2-class directories, GitHub, Product Hunt, and LinkedIn with cross-linked references.
  • Stage 4: Ensure prompt discoverability through cross-LLM testing and prompt-anchored content.
  • Stage 5: Publish in AI-indexed channels such as GitHub, Substack, Medium, Hugging Face, and LinkedIn.
    • New 5.11: Visual Search and Multi-Modal Asset Readiness.
  • Stage 6: Submit to LLM indexing and discovery tools, now including visual sitemaps and asset feeds.
  • Stage 7: Create AI ecosystem profiles such as Custom GPTs and Hugging Face Spaces.
  • Stage 8: Establish trust signals and cross-linking integrity.
  • Stage 9: Monitor, iterate, and maintain visibility with cadence and logs.

4. Multi-Modal Extension in v2.2

4.1 Stage 5.11: Visual Search and Multi-Modal Asset Readiness

Objective: Make images and videos machine-readable, attributable, and retrievable by visual search systems that feed assistant answers.

Checklist highlights

  • File hygiene: Descriptive, human-readable filenames; avoid opaque camera IDs.
  • Accessibility text: Natural-language alt text and captions with truthful context.
  • Structured data: ImageObject for images; Product with image for catalogued items; transcripts for video and audio.
  • Brand recognition: Multiple angles, subtle brand elements, and logo recognition tests.
  • Discovery infrastructure: Dedicated image sitemap, submission in Google and Bing consoles, visual QA via Lens-style tools.
  • Visual prompt testing: Record brand presence for image-seeded queries in a prompt tracker.

Rationale: Visual search now drives AI Overviews, carousels, and featured results. Treat images as primary answers, not only as page decorations.

4.2 Stage 6 Cross-Reference: Visual Indexing Submissions

Submit optimised visual assets to discovery engines and open repositories. Capture receipts or snapshots that confirm indexing. This converts asset optimisation into retrievable presence inside assistant pipelines.

5. Measurement: Metrics and Audit Criteria

AIVO certification requires proofs that are reproducible and model-agnostic.

5.1 Prompt Visibility Rate (PVR)
Share of tested prompts where the entity is mentioned or recommended by an assistant. Logged per model and per prompt cluster.

5.2 Citation Density Score (CDS)
Count and quality weight of citable reference units across trusted ecosystems. Includes open repositories, directories, and structured sources.

5.3 Visual Asset Readiness Score (VARS)
Composite of filename hygiene, alt text coverage, schema completeness, multi-angle coverage, brand recognition tests, sitemap submission, and visual prompt confirmation.

5.4 Cross-LLM Metadata Parity Index (CMPI)
Degree of alignment across titles, descriptions, categories, and entity fields in ecosystem profiles and structured data seen by GPT-class models and peers.

5.5 Retrieval Freshness Interval (RFI)
Median days between asset or metadata updates and confirmed re-index events.

Certification evidence pack

  • Target prompt map with linked assets.
  • Live schema and Wikidata references.
  • Listings across at least three mention-graph platforms.
  • Ten prompt tests with model-specific results and screenshots.
  • Multi-modal proofs for Stage 5.11 and visual indexing confirmations under Stage 6.
  • Trust signal inventory with cross-linking checks.
  • Monitoring logs that show cadence compliance.

6. Sector-Specific Guidance

High-trust sectors such as healthcare, finance, insurance, legal, and education require additional signals: sector schema types, regulatory registrations, peer-reviewed references, and disclaimers in Custom GPTs and Spaces. Prompts should surface compliance and safety attributes, not only category fit.

7. Governance, Ethics, and Safety

AIVO endorses transparency, authenticity, responsible optimisation, accessibility, and policy alignment. Avoid fabricated reviews, manipulative prompt stuffing, and deceptive impersonation. Maintain clear ownership metadata and ensure inclusive access.

8. Discussion

Assumptions tested:

  • Visibility is not equivalent to page rank. Assistant recall depends on entity clarity, citation fidelity, and structured evidence.
  • Visual readiness is not optional. In image-seeded or mixed-modal queries, assets can be the entire answer.
  • Over-indexing on one ecosystem is fragile. Redundancy across text and visual profiles reduces volatility when models update.

Risks:

  • Misattribution when brand signals are weak.
  • Decay in visibility without refresh cadence.
  • Sector compliance gaps that suppress assistant recommendations.

9. Limitations

This methodology does not cover paid integrations or proprietary ingestion pathways that are not publicly documented. Third-party model behaviour can change without notice, which is why Stage 9 mandates continuous monitoring.

10. Future Work

Priority items include: automated visual prompt testing harnesses, open benchmarks for VARS and CMPI, and standardised artefact packages for model-friendly ingestion across text, image, and video.

11. Conclusion

AIVO Standard v2.2 operationalises multi-modal AI visibility. It treats images and video as first-order evidence for assistant recommendations and builds auditable pathways from asset optimisation to assistant recall. Organisations that implement the Stage 5.11 checklist and Stage 6 submissions, alongside the full nine-stage lifecycle, gain durable presence across models and modalities.

References

  1. AIVO Methodology v2.2: Methodology for AI Visibility Optimization. AIVO Standard. 12 August 2025. Public Draft submitted for peer review.
  2. schema.org. Structured Data Vocabulary.
  3. Wikidata. A Free and Open Knowledge Base.
  4. Google Lens, Bing Visual Search, Pinterest Lens. Visual search systems used for recognition and discovery.
  5. Open ecosystems cited by AIVO: GitHub, Hugging Face, Medium, Substack, Crunchbase, G2-class directories.

Appendix A: Stage 5.11 Visual Readiness Checklist (Abbreviated)

  • Filenames are descriptive and human-readable.
  • Alt text and captions are truthful and contextual.
  • ImageObject or Product schema with image is present.
  • Multiple angles with subtle brand elements exist.
  • Image sitemap submitted in Google and Bing consoles.
  • Visual prompt tests recorded with screenshots.

Recommended citation:
AIVO Standard Research Group. AIVO Standard v2.2: A Canonical Framework for Multi-Modal AI Visibility. AIVO Journal, August 2025.

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