What framework to use for increasing visibility in AI Search

In the current digital environment, achieving content success requires strategies that extend beyond keyword optimisation and short-term engagement metrics.

It demands a sophisticated understanding of how search engines and LLMs interpret and retrieve information, alongside the timeless imperative of captivating human audiences.

The key isn’t just to produce content; it’s to design it for entity alignment, AI retrievability, and human engagement.

Here’s how a fictional My Mini Painting brand targeting the niche market of mini painting hobbyists and professionals can implement a process for its visibility growth strategy, utilising a framework (let’s call it the “AI Search Optimisation Framework”) that I already use with my clients.

This framework aims to increase visibility in AI Overviews, strengthen brand recognition and salience, and support engagement with the miniature painting community. It is composed of eight steps, which I now briefly introduce in the following table:

Now, let’s examine each step in detail.

Step 1: Advanced Brand Ontology Design for Semantic Depth

Define the Purpose and Scope Before Mapping

The initial approach to designing a brand ontology within the AI Search Optimisation Framework starts by mapping the domain: painting techniques, materials, tools, miniature lines, lore themes, and competitions.

Before building that map, it’s essential to clarify why the ontology is needed.

It is needed because a well-scoped ontology guides its structure.

Ask:

• Is the goal to improve internal content discoverability?
• Is it to enhance search engine comprehension?
• Is it to enable personalised user experiences?

For My Mini Painting, the question becomes: “What particular challenges will this ontology address?”

For example, it might aim to ensure that AI systems accurately distinguish between Object Source Lighting (OSL) and Non Metallic Metal (NMM) as distinct advanced painting methods, not interchangeable keywords.

This foundational clarity should be formalised using SMART goals (Specific, Measurable, Achievable, Relevant, Time-bound).

Go Beyond Lists: Define Semantic Relationships

Once the purpose is clear, expand beyond listing categories to define how concepts relate.

Ontologies are not taxonomies. They don’t just describe hierarchies, but they encode meaningful relationships between entities.

Hierarchical Example

“Object Source Lighting (OSL)” → “Lighting Techniques” → “Advanced Painting Methods” → “Miniatures for Display”

But real semantic value comes from explicitly modelling relationships such as:

This level of modelling helps AI systems connect disparate facts meaningfully.

Ground the Ontology in Real-World Knowledge

To ensure the ontology is not theoretical but practical:

• Involve domain experts like competitive painters or lore specialists.
• Use AI tools to identify and cluster entities and their probable relationships.
• Treat AI suggestions as a starting point, not a final product.

This combination of automation and human expertise builds an ontology rooted in the actual knowledge graph of the miniature painting community.

Plan for Continuous Maintenance

The miniature painting world evolves constantly:

• New techniques emerge.
• Paint ranges are updated.
• New miniature lines are released.

Your ontology must be adaptable. Set a review cadence (e.g., quarterly) to ensure it stays current. Each review should integrate:

• Community trends.
• Game updates.
• New brand terms or product lines.

Enable AI Systems to “Think Like a Painter”

An ontology gives AI models a blueprint of your brand’s specialised knowledge.

For example: “Object Source Lighting (OSL)” → “Advanced Painting Method” → “Miniatures for Display”

This tells the model how to group, recommend, and relate content, rather than relying on keyword proximity.

Why it matters:

• Increases AI-generated content precision.
• Strengthens your brand’s association with niche, valuable topics.
• Boosts inclusion in AI Search and entity-based search.

Model Complex, Multi-Entity Queries

Let’s take a real user query: “How to paint gold NMM on Stormcast Eternals”

This query implies:

• Technique: NMM
• Material/effect: Gold
• Miniature: Stormcast Eternals

If the ontology explicitly maps these links, your content can precisely match user intent, and AI systems will know it.

That’s how you win long-tail, high-relevance queries that blend technique, material, and model, and become the go-to resource in your niche.

Semantic Mapping: Table Example

To illustrate the depth of an ontological approach, here’s a tangible example of entity-to-entity relationships:

Step 2: Precision Entity Identification & Strategic Alignment

Move from Recognition to Semantic Strategy

The AI Search Optimisation Framework includes the identification of target entities and the use of extraction tools.

To advance this step, the goal is not only recognising entities but strategically aligning them with how AI systems and search engines interpret meaning.

This requires connecting entities to conceptual frameworks such as E-E-A-T (Expertise, Experience, Authoritativeness, Trustworthiness), not as ranking signals, but as semiotic markers of clarity, trust, and domain understanding.

Prioritise Entities by Semantic Relevance

Not all entities are equally strategic.

While identifying all relevant entities is useful, focus should fall on those that:

• Reflect editorial or commercial intent.
• Are semantically central to the brand’s domain.
• Support distinctive expertise or positioning.

Use Keyword Research as a Proxy

Keyword research is valuable here, not to estimate traffic, but to identify which entities are popular, familiar, or conceptually significant within the search language of your audience.

For example, rather than simply noting “Acrylic Paints,” consider specific subtypes (e.g., heavy body acrylics, transparent acrylics) or brands (e.g., Vallejo Model Color) that frequently surface in queries.

Expand with Semantically Related Phrases

Once primary entities are mapped, enrich the content with:

• Synonyms.
• Paraphrases.
• Thematically adjacent terms

This expands the semantic surface area of the topic and reinforces its connections in AI-driven systems.

For instance, around Non Metallic Metal (NMM), consider including:

• “metallic sheen without metallics”
• “simulating metal with matte paints”
• “light source placement for NMM”
• “NMM colour theory”

These terms provide latent reinforcement without keyword stuffing.

Enhance Entity Context with Knowledge Graph

Google’s Knowledge Graph API helps surface how entities are already structured, categorised, and connected in the search ecosystem.

For example, the entity Wet Blending may be associated with:

• “smooth transitions”
• “miniature painting techniques”
• specific named artists or brands

Knowing these associations allows My Mini Painting to contextualise entities in line with Google’s understanding, increasing content alignment and retrievability.

Audit Competitors for Entity Gaps

Use competitor analysis to:

• Identify entity coverage trends.
• Spot gaps in your content.
• Discover emerging entities gaining traction

Example: If competitors rank for acrylic paint drying retarders and My Mini Painting has no content on the topic, this signals an actionable gap in entity coverage.

Treat E-E-A-T as a Semiotic Framework

E-E-A-T should not be viewed as a ranking system, but as a conceptual model for signalling credibility, experience, and contextual authority.

Ways to apply this:

• Reference named entities (e.g., Golden Demon, Games Workshop, Winsor & Newton).
• Attribute techniques to recognised practitioners.
• Show specificity and precision in how entities are used.

These practices communicate semantic trustworthiness to both users and AI systems.

Signal AI Trust Through Precise Entity Usage

Entity precision is a core trust signal for generative systems like Google’s AI Overviews.

By consistently referencing structured entities like Citadel Layer Paints or Rebel Pathfinders, and defining their relationships in line with your ontology, you:

• Reduce ambiguity for AI.
• Improve interpretability.
• Increase chances of selection in featured responses

Prepare for the Zero-Click, AI-Driven Search

In a post-click search environment, AI serves direct answers.
To surface there, your content must be:

• Extractable in structure.
• Aligned with AI’s conceptual map.
• Semantically unambiguous

The best strategy is to use Schema, Knowledge Graph references, and internal consistency to match how AI “already knows” the entity, so reinforcing its place in your content.

This makes My Mini Painting not just discoverable, but summarizable and visible, even when users don’t click through.

Step 3: Optimising Taxonomy for Comprehensive Topical Authority

From Hierarchy to Semantic Strategy

The basic hierarchical taxonomy within the AI Search Optimisation Framework already organises content effectively.
However, we can enhance its strategic value by exploring more flexible taxonomy models and applying best practices in SEO, UX, and AI retrievability to maximise topical authority.

Align Taxonomy with Ontology

Taxonomy and ontology serve different purposes but must be tightly integrated.

• Ontology defines conceptual relationships (e.g., “Wet Blending” is a type of “Technique”).
• Taxonomy structures these concepts for navigation (e.g., placing “Wet Blending” under “Techniques”).

The taxonomy should directly reflect the ontology’s logic to ensure consistency across categories, tags, and filters.

Move Beyond Pure Hierarchy

Miniature painting is a complex domain. A strict hierarchy can limit discoverability. On the contrary, a hybrid taxonomy offers greater flexibility:

• Hierarchical categories (e.g., Techniques > Advanced).
• Tags for cross-cutting themes (e.g., OSL spanning multiple areas).
• Faceted filters (e.g., Paint Type: Acrylic, Brand: Citadel).

This allows users — and AI systems — to find content through multiple intuitive paths.

Optimise Taxonomy for SEO and Internal Linking

Each category and subcategory page is a strategic SEO asset.

To maximise topical authority:

• Include relevant keywords in headings, metadata, and body content.
• Add introductory context to category pages (not just lists).
• Internally link to child and sibling categories

Example: The Advanced Painting Methods category should define its scope and link out to OSL, NMM, and Weathering, reinforcing both user understanding and search engine signals.

Use Descriptive and Consistent Naming Conventions

Taxonomic clarity starts with naming.

• Use precise, human-readable labels (e.g., “Advanced Painting Techniques” instead of “Pro Tips”).
• Standardise names sitewide (e.g., always use “Star Wars: Legion”, not “SW Legion”)

If alternate labels are needed, define them in the ontology and redirect or clarify as appropriate.

Design for UX: Intuitive and Multi-Path Navigation

A well-structured taxonomy supports usability:

• Keep structures shallow, intuitive, and logically grouped.
• Use breadcrumbs and clear labels.
• Enable logical entry points from different perspectives

A user searching “how to paint a tank” should be able to reach content via “Game Systems > Warhammer 40,000 > Vehicles” or “Techniques > Weathering > Chipping Effects”.

Taxonomy as AI’s Internal Map

Taxonomy is not just for users; it is AI’s navigational schema for your content.

• Grouped content reinforces topical depth.
• Optimised category pages act as semantic gateways.
• Logical internal linking clarifies domain expertise.

When AI systems crawl and index the site, they interpret structured taxonomy as a signal of comprehensive coverage, and not just a collection of pages, but a coherent knowledge system.

This elevates My Mini Painting as an authoritative source across broader query spaces like Miniature Painting Techniques.

Anticipate Query Fan-Out with Facets and Tags

AI systems often expand queries into multiple sub-intents: e.g., “best paint for minis” → [by brand, by finish, by use case, by competition].

A hybrid taxonomy ensures retrievability along all these axes:

• Facets: Paint Finish: Matte, Miniature Type: Vehicle.
• Tags: Golden Demon Prep, Weathering Challenge, OSL Showcase

Anticipating these facets makes content more matchable to AI-generated queries, especially in conversational or zero-click search environments.

Step 4: Mastering Query Mapping & Full Intent Coverage

Go Beyond Keywords: Map Semantic Intent

The framework I suggest includes advanced query mapping techniques like:

• People Also Ask (PAA).
• Related searches.
• Query expansion and refinement.
• Query rewriting.
• Query fan-out

The refinement is to apply these methods systematically, ensuring My Mini Painting aligns not just with user keywords, but with AI’s interpretation of intent clusters.

Understand and Anticipate Query Fan-Out

AI systems routinely expand a single query into multiple implicit and related ones to fully understand the user’s intent.

For each core topic or entity, My Mini Painting should proactively map potential fan-out queries.

Example:

Core Query: “Non Metallic Metal (NMM)”
Fan-Out Queries:

• NMM colour palette.
• Best brushes for NMM.
• NMM for beginners.
• NMM vs. OSL.
• NMM on power armour.
• NMM blending techniques.
• Troubleshooting NMM streaking.

Covering this spectrum ensures content is not bypassed by AI when parsing for related information.

Optimise for Query Rewriting Scenarios

Google often rewrites user queries to improve precision and relevance.

My Mini Painting, then, must anticipate and address these rewrites by using a diverse, semantically rich vocabulary.

Example:

• User search: “paint miniature shiny”.
• Google rewrite: “how to achieve metallic effects on miniatures” or “true metallics vs. NMM”.

By embedding synonyms and conceptual equivalents, the content remains retrievable, even when the original phrasing differs.

Expand Content to Cover the Full Intent Space

To succeed in AI Search, content must address not just how-to questions, but the entire intent cluster.

This includes:

• Product selection advice.
• Brand comparisons.
• Common mistakes.
• Technical breakdowns.
• Niche use cases.

Example for Wet Blending:

• Best paints for wet blending.
• Common wet blending mistakes.
• Wet blending vs. glazing.
• Wet blending for smooth skin tones.
• Wet blending on large surfaces

AI-Search-friendly content is intent-complete, not just task-specific.

Use AI-Powered Tools for Query Discovery

Tools can significantly streamline query mapping:

• AlsoAsked→ PAA relationships.
• Keyword Insights → clustering.
• Semrush / Ahrefs → high-volume queries, keyword intent, and SERP analysis.

These tools (as well as others) help identify:

• High-priority queries.
• Variants across user personas.
• Gaps competitors haven’t filled.

Validate Mapping with Real Search Data

After building out your intent clusters:

• Cross-check them with search volume.
• Prioritise based on business relevance.
• Focus on where you can offer unique, high-authority responses.

This ensures the mapping effort translates into visibility and conversions, not just theoretical coverage.

Pre-Optimise for AI Search

Proactive query fan-out mapping functions as pre-optimisation for AI-generated answers.

Strategy:

• Anticipate the fan-out from a single prompt.
• Create content that addresses each sub-query.
• Ensure the format is extractable and structured

This makes My Mini Painting content:

• Retrieval-ready.
• Summarizable by AI.
• Citable in multiple spots within one synthetic answer.

AI systems prefer content that’s already semantically organised and multifaceted.

Step 5: Building Topical Content Hubs for AI Authority

Use Topical Hubs to Signal Domain Expertise

Topical hubs — such as the Advanced Techniques Hub or the Star Wars: Legion Painting Hub — are powerful tools to structure content for both SEO and AI comprehension.

These hubs act as semantic containers that show both breadth and depth on a subject.

Each hub should be built using:

• A broad, entity-anchored pillar page.
• Multiple focused cluster contents (not necessarily only articles).
• Strong internal linking between them

Anchor Pillars in Core Entities and Broad Intent

The pillar content provides a high-level overview of the topic.

For instance, The Complete Guide to Advanced Miniature Painting:

• Defines what “advanced” means.
• Introduces major techniques (OSL, NMM, Weathering).
• Explains use cases and applications

This content becomes the central node for all related pages.

Use Clusters to Answer Specific Fan-Out Queries

Each cluster content should explore one entity or subtopic in depth.

Examples under the Advanced Techniques Hub:

• OSL Techniques → covers “OSL light source placement,” “OSL on power weapons,” “OSL colour theory”.
• Weathering for Dioramas.
• NMM Guides.

Examples under the Star Wars: Legion Painting Hub:

• Painting Rebel Pathfinders for Display.
• Clone Trooper Colour Schemes.

These articles reflect the query fan-out insights from Step 4.

Strengthen Semantic Signals with Internal Linking

A well-linked hub signals topic coherence and editorial intent.

Best practices:

• Link from the pillar to each cluster.
• Link from each cluster back to the pillar.
• Link between related clusters.

For example, NMM Guides should link to the OSL Techniques and the Advanced Miniature Painting Pillar.

This creates a dense semantic network that reinforces topical authority.

Reinforce E-E-A-T Through Depth and Structure

Topical hubs naturally demonstrate:

• Expertise: Specialised, in-depth cluster articles.
• Experience: Practical, detailed examples and tutorials.
• Authoritativeness: Structured, cross-linked ecosystem.
• Trustworthiness: Clarity, consistency, and factual depth.
  

AI systems use this structure to determine domain-level trust, not just page-level relevance.

Hubs Define AI’s Knowledge Domains

AI systems analyse content at the passage level but evaluate authority at the topic level.

A standalone article might rank, but a structured hub:

• Shows intentional coverage.
• Signals editorial hierarchy.
• Becomes AI retrievable for broader queries
  

This makes My Mini Painting a preferred source when AI builds answers around complex or multifaceted queries.

Human Editorial Structure Outperforms AI Summaries

AI can summarise, but it cannot curate, interrelate, and structure content into a comprehensive topical hub.

By building:

• High-quality pillar pages.
• Entity-informed cluster articles.
• Dense internal link graphs.
  

My Mini Painting creates meaningful, irreducible content that’s valuable to users and favoured by AI.

This gives the brand a durable edge in an environment where generative AI increasingly shapes visibility.

Step 6: Granular Content Adaptation for Diverse Personas & SERP Features

Expand Persona Modelling Beyond Demographics

Effective content begins with deep persona understanding.

Go beyond age or skill level and map motivations, challenges, and preferred formats.

Examples:

• Beginner Hobbyist
  Seeks: quick wins, affordability, basic theory
  Prefers: short guides, video, visual cues
• Competitive Painter
  Seeks: technique depth, product reviews, judging insights
  Prefers: detailed tutorials, charts, long-form content

Content planning must reflect these variations in tone, structure, and depth.

Integrate Sentiment and Intent Nuance

Search queries imply emotional context. Content should respond accordingly.

Examples:

• “Why are my paints clumping?”
  → Signals frustration → Requires reassurance and clear troubleshooting
• “Golden Demon 2025 entry prep guide”
  → Signals ambition → Requires advanced, technical, goal-driven advice

Match the tone and resolution path to the user’s emotional state and urgency.

Align Content Design with Human-Centred Principles

Design for clarity, engagement, and task completion.

Format and tone adaptation:

• Easy Wet Blending for First-Time Painters
  → Simple language, short steps, annotated visuals
• Advanced Glazing for Photorealistic Skins
  → Technical vocabulary, visual references, deeper theory

Content should speak in the language of its intended persona, not a one-size-fits-all style.

Optimise for SERP Features and Multimodal Reach

Modern SERPs are not uniform, and they favour different content types depending on intent.

Match format to SERP intent:

• Image packs → Optimise high-quality, descriptive images.
• People Also Ask → Use concise answers, lists, or steps.
• Video carousels → Include embedded tutorials or shorts

Monitor SERPs for each entity-query pair and design content accordingly.

Extend Distribution Beyond Google

Search isn’t the only discovery engine.

Platforms like YouTube, TikTok, Reddit, and email newsletters offer access to real audiences and less reliance on AI intermediation.

Consider, for instance:

• Creating repurposed short video content for TikTok/Instagram/YouTube.
• Sharing guides on Reddit hobbyist threads.
• Launching a segmented newsletter for different personas.

Build Direct Audience Relationships

AI may mediate discovery, but trust is earned through connection.

My Mini Painting should cultivate:

• Community forums or Discord channels.
• Exclusive workshop content.
• Personalised email sequences.

These initiatives reinforce brand memory, even in a zero-click or AI-first ecosystem.

Persona Precision Improves AI Relevance Scoring

AI attempts to infer who the user is and what they need.

By tailoring content to reflect specific personas, My Mini Painting can signal to AI:

“This content is made for exactly that user.”

Example:

“Easy Wet Blending for First-Time Painters”
→ Narrow intent + targeted tone = high relevance score

Even if AI doesn’t generate a click, it’s more likely to select this content as the best match for a synthetic or reformulated query.

Design for AI-Usable Multimodal Output

AI Search, and in particular AI Overviews and AI Mode, is inherently multimodal, pulling in:

• Video.
• Image.
• Tables.
• Structured data (FAQs, steps, snippets).

Strategic content formats:

• Video tutorials for techniques.
• Comparison tables for brushes or paints.
• Annotated images for visual guides.

This ensures content is retrieval-ready, no matter how AI decides to present the answer.

This table is a good synthetic representation of what was just said:

Step 7: Crafting Content for Optimal AI Retrievability & Human Connection

This step is at the core of creating AI-ready content within the AI Search Optimisation Framework.

The existing use of “answer units” and structured data is already effective. The refinement here involves reinforcing modular design, embedding semantic signals, and prioritising original, human-led insight that AI cannot replicate.

Apply the Inverted Pyramid at Both Macro and Micro Levels

Use the Inverted Pyramid structure not only at the macro level (overall page) but also at the micro level (each section and paragraph).

Each unit of content — whether a full article or a single section — should begin with the core answer or takeaway, followed by supporting detail, nuance, and optional depth.

Example:

In “How to Achieve Smooth Blends with Oil Paints”, the first sentence should directly answer the “how” before listing techniques, tools, and troubleshooting steps.

This structure:

• Makes content highly scannable for humans.
• Enables passage-level extraction by AI.

Enhance Chunking with Clear Headings and Structured Formats

Structure your content into modular, self-contained sections, each with descriptive subheadings.

Use:

H2 / H3 headings for chunk delineation
Bullet points and numbered lists for clarity
Tables for side-by-side comparisons
Example: A guide on Cleaning Your Airbrush should be chunked into sections like Disassembly, Rinsing, Deep Clean, and Lubrication, each functioning as a standalone retrieval unit.

Use the Right Schema Type to Define Content Meaning

Don’t stop at the FAQ schema. Choose the schema type that best reflects the function and meaning of each content asset.

Examples:

• HowTo → for tutorials and step-by-step walkthroughs.
• Product → for product reviews or evaluations.
• Review → for third-party opinions.
• PodcastEpisode → for audio-based content.
• ImageObject → for optimised standalone visuals.
• Article → for general editorial content.
• VideoObject → for embedded or hosted videos

Schema not only helps systems understand what a page is about, but it also signals how to use it.

Additionally, always use @id fields to uniquely identify and interlink structured data entities (e.g., authors, products, articles).

This builds a consistent semantic web across your site and helps AI systems:

• Resolve entities.
• Connect context across pages.
• Strengthen your perceived topical authority

Keep E-E-A-T Integration Consistent (See Step 2)

As outlined in Step 2, build trust signals naturally into the content through:

• Real author bios and credentials.
• Credible citations or source links.
• Firsthand experience or outcomes

There’s no need to restate the full conceptual framework here; just ensure these principles are embedded in every piece, especially when authority matters (e.g., reviews, comparisons, advanced techniques).

Maintain Human Focus: Plain Language and Accessibility

Even with AI optimisation in mind, write for humans first.

Key practices:

• Use plain, approachable language.
• Favour an active voice and short sentences.
• Avoid unnecessary jargon.
• Ensure visual clarity (e.g., contrast, alt text, font hierarchy)

This increases content accessibility and also improves dwell time and engagement signals Google uses for evaluation.

Share What Only Humans Can: Originality, Context, Insight

AI can summarise.
It cannot:

• Tell personal stories.
• Offer contextual judgment.
• Share firsthand experimentation.
• Articulate failure, frustration, or inspiration

My Mini Painting should highlight its unique artistic methods, team experiences, and behind-the-scenes insights, aka the things AI cannot synthesise from public data.

This originality makes content “worth quoting,” not just summarising (and people remembering you and returning directly to your website).

Step 8: Ongoing Measurement & Iteration. Sustaining AI & SEO Performance

The final step in the AI Search Optimisation Framework focuses on establishing a dynamic feedback loop.

Below, I expand the scope of measurement to explicitly track AI Search-specific signals, align performance with brand positioning, and ensure recency-driven iteration as a core practice.

Measure AI Share of Voice, Not Just Clicks

In the AI Search and zero-click search era, visibility is essential.

As a consequence, a new, strategic KPI is emerging: AI Share of Voice, which is the frequency with which your brand is cited, mentioned, or surfaced in AI-generated answers across relevant topics.

For My Mini Painting, this includes tracking AIO/AI Mode and LLMs’ synthetic answers for topics such as, for example:

• Miniature painting techniques.
• Best miniature paints.
• Star Wars: Legion painting tutorials

Monitoring whether My Mini Painting appears more or less often than competitors reveals how AI perceives topical authority, and how your brand stacks up in semantic prominence.

AI Share of Voice can be broken down into:

• Overall brand visibility across AI-generated content.
• Mentions by topic, subtopic, or entity.
• Contextual association with desired brand values or topics.

This measurement shift redefines what success looks like in content marketing:

Not just driving clicks but becoming the go-to cited source within AI systems.

Track Brand Authority at Multiple Levels

Brand authority is not monolithic. It should be evaluated across several interrelated dimensions:

These metrics collectively indicate how visible, trusted, and semantically relevant your brand is, not only to users, but to AI systems parsing meaning across the web.

Recommended Tools for Monitoring

To implement this tracking, the following tools can support different layers of measurement:

• Semrush, Ahrefs, Search Console → For branded search trends, topical rankings, and competitor overlap.
• Advanced Web Ranking → For Share of Voice tracking in SERPs, both classic and AI.
• Waikay → For topic visibility in LLMs.
• Gumshoe.ai → For measuring brand mentions across persona-AI-generated responses.
• Optional: Custom SERP scraping and LLM prompt testing for qualitative audits

Audit Brand Alignment in AI Perception

Measuring how AI describes your brand is just as important as whether it mentions it.

If My Mini Painting wants to own “Advanced Competitive Painting,” but AI consistently frames it as “beginner-friendly tips,” there’s a semantic gap.

This gap indicates a content mismatch, not an authority failure, and can be corrected by:

• Adjusting internal linking to elevate advanced content.
• Reinforcing entity co-occurrence in high-authority sections.
• Updating pillar pages to clearly state brand positioning

Use branded queries (e.g., My Mini Painting NMM) to test how AI and traditional SERPs frame your content.

Focus on Recency and Relevance

Content freshness is a major trust signal for both users and AI.

Prioritize updating over always creating from scratch.

Recency enhances retrievability in AI systems, especially for time-sensitive or competitive topics.

For example:

• The Golden Demon Entry Prep Guide must be updated each year with new dates, criteria, and examples..
• Old Basic Techniques guides should be reviewed for accuracy, tool updates, or evolving best practices.

Your entire taxonomy and content architecture should be audited on a rolling schedule, ensuring that evergreen, seasonal, and emerging topics all remain aligned with evolving AI prompts and user behaviour.

Expand Multichannel Authority

While Google remains essential, AI-driven platforms are reducing click-throughs.
Strengthen My Mini Painting’s brand by meeting the audience where they are:

• YouTube → For in-depth tutorials
• TikTok / Instagram Reels → For short-form painting tips
• Reddit → For peer credibility in painting communities
• Newsletters / Podcasts → For direct relationships beyond search
• Discord → For nurturing and building community loyalty

This builds durable awareness even if search traffic dips and contributes to off-site signals that AI uses for trust and entity profiling.

Connect Content with Business Outcomes

Every content piece should map to a business goal, even if it’s indirectly.
Whether that’s selling a brush set, promoting a workshop, or capturing an email, intent-aligned CTAs are critical.

AI-ready content must do more than answer. It must:

• Educate.
• Engage.
• Guide the user toward action.

Example: An Advanced Glazing guide should subtly suggest relevant brushes, promote a workshop, or offer downloadable guides.

Conversion points don’t need to be aggressive, but they must be intent-relevant and discoverable.

Create a Culture of Iteration

Your content strategy is not static. The AI Search Optimisation Framework must be treated as a living system, constantly refined based on:

• AI’s evolving retrieval patterns.
• Shifts in SERP features.
• Changes in brand strategy or user behaviour

This involves:

• Scheduled content audits.
• Ongoing AI prompt testing.
• Schema enhancements (e.g., adding missing @id connections).
• Refreshing internal links to reinforce topical hubs

Success in AI Search isn’t about chasing their always-evolving algorithm.
It’s about building an adaptive, resilient system that mirrors your authority and audience’s evolving needs.

Final Thought: Be Worth Citing

Content that earns AI citations is:

• Clearly structured.
• Topically deep.
• Aligned with entities.
• Authored with experience.
• Visibly updated.
• Rich in nuance.

Sustainable visibility in AI Search is not about tricking the machine.
It’s about creating content so useful, trustworthy, and original that AI wants to quote it and users want to visit.

Recognition is the “keyword”.