Mitesh Patel

SEO is the familiar work of ranking product pages on Google through keywords, backlinks and optimized descriptions.

• They keep it as the foundation rather than declaring it obsolete.
• AEO means appearing in the AI Overview answer at the top of a results page, so a brand can rank third and still be absent from the only part of the page many shoppers read.
• GEO is the newest layer: getting an assistant such as ChatGPT to recommend a product by name when someone asks for a recommendation instead of a list of links.
• Each layer gets its own tactic: keep optimizing product pages and site speed for SEO, build FAQ pages, comparison guides and "best X for Y" articles for AEO, and earn mentions in reviews, Reddit threads, niche publications and expert roundups for GEO.

Patel describes the shift from "rank higher, get clicks, maybe convert" to "be the answer, get recommended, win the sale," using queries a shopper would actually type: someone searching for the best running shoes for flat feet, or asking an assistant for a good moisturizer for dry skin.

The model treats SEO as still necessary, not dead, tempering the stronger claim elsewhere that people no longer scroll and simply trust the first AI answer. Ask for performance data behind either claim.

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A validation step now sits in the middle, where buyers use an AI assistant to synthesize and compare options before searching.

• They call the metric for that step "share of model": a brand missing from the model’s answer is unlikely to win the impressions and clicks that follow.
• Reviews and customer retention feed the answers, making existing customers material for future recommendations.
• They present the pattern as holding across ecommerce, media, fintech, pharma and B2B SaaS, and call it the blueprint for 2026 and beyond.

To illustrate the argument, Patel built a diagram of the revised funnel with Google Gemini 3 Pro (the Imagen 3 model), labelling the stages and metrics in the post, including the numbered synthesis layer and the trust loop.

This extends the three-layer search argument into a measurement claim: visibility in AI answers becomes something a brand can own and track, not just a tactic.

• The post presents the framework as a prediction.
• Ask for data from the industries it names.

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They estimate this costs about 20 minutes before any real work begins.

• The fix is to write the creative process once as a Skill, a simple markdown file the assistant reads automatically when a matching task appears.
• Skills compose, so research, campaign planning and copy steps trigger each other and build inside one conversation instead of living in disconnected projects.
• They can also be shared across a team for consistent output.

The post offers a playbook in exchange for liking the post and commenting "SKILLS," with a follow required before Patel can send it by direct message.

• They list its contents: the architecture for how Skills trigger and compose, five ready-to-use templates built for DTC and agency teams, step-by-step setup, guidance on writing instructions that produce consistent output, and a composability framework for multi-step creative workflows.
• Ask for the playbook to review the templates and setup steps.

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Patel opens with a short claim that current practice is outdated, such as "there are now three ways to show up in search" or "your marketing funnel has a new layer," then names the layers or numbered stages and ties a short list of actions to each one.

• Headings and arrows make the framework scannable, with everyday examples a brand would recognize.
• The posts close with a free playbook distributed through a comment keyword, a format that appears in the Claude Skills post.
• This structure captures how Patel thinks: diagnose the new step, make it nameable and give the reader something to do about it.

The three posts move from a search framework with one tactic per layer, to a funnel diagram with three labelled stages, to a workflow post that names the failure it removes and lists the playbook contents. The playbook handoff is gated: like the post, comment the keyword and follow the account to receive the material.

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A newcomer who reads only one post should start here to understand what they mean by AI visibility.

The post splits search into three jobs.

• SEO is the familiar one: ranking product pages on Google with keywords, backlinks and optimised descriptions, which Patel says still matters but is no longer enough on its own.
• AEO covers AI Overviews, where Google answers a query like "best running shoes for flat feet" at the top of the page.
• A brand that ranks third can still be invisible if it is not inside that answer.
• GEO is the newest: getting an AI assistant to recommend a product by name when a shopper asks directly, since the assistant gives one or two answers rather than ten links.
• They sum up the shift as moving from rank higher, get clicks, maybe convert, to be the answer, get recommended, win the sale.
• The post closes with one action per channel: keep optimising pages, structure content as direct answers to specific questions, and get mentioned where AI learns, naming reviews, Reddit threads, niche publications and expert roundups.

For brands planning a campaign

Useful for scoping a talk or consulting engagement on AI visibility: it gives brands three concrete workstreams to budget for—page-level SEO, question-shaped content for AI Overviews, and a mention-building programme in third-party sources. Treat the actions as a proposal to test, not as measured results.

It answers what actually changed in the buyer journey, rather than only listing tactics.

Patel argues the funnel is no longer a straight line from awareness to search, because a validation step now sits in the middle: people use an AI assistant to synthesise and compare options before they run a search.

• The post names a metric for that step, "share of model", and claims that a brand which does not own it will not win the impressions and clicks that come after.
• It also describes a loop in which retention and reviews feed the answers AI gives, so existing customers become the material that trains future recommendations.
• They say the pattern holds across ecommerce, media, fintech, pharma and B2B SaaS, and note the accompanying diagram was produced with an AI image model.
• The post frames this as a claim about 2026 and beyond.

For brands planning a campaign

This gives a campaign tracking presence inside AI answers a measurable target: ask a partner to track that presence, not just rankings and click-through. A brand could test the claim cheaply by checking how often its product appears in AI responses for a short list of buying questions before committing to a larger programme.

It explains how they approach AI-assisted marketing production day to day, distinct from the search-visibility argument in the other posts.

The post describes a recurring problem for ecommerce and agency teams using AI for creative work: every new conversation starts from zero, because brand guidelines, the campaign brief format and context have to be pasted in again, and Patel estimates this burns around 20 minutes before any real work begins.

• The proposed fix is to write the creative process once as a "Skill", described as a simple markdown file that the assistant reads automatically when a relevant task comes up.
• Patel says Skills compose, so research, campaign brief and copy steps trigger each other and build in one conversation, and that they can be shared across a team for consistent output.
• The post outlines a paid or gated resource behind it: a playbook is offered free in exchange for liking the post and commenting "SKILLS", with a follow required for the link.
• The post describes the architecture but does not contain the templates or setup steps themselves.

For brands planning a campaign

Points to a concrete consulting angle: reducing repeated setup time in AI-assisted content production, which a brand can measure before and after. The substantive material sits behind a comment-gated offer; ask for the delivered document to evaluate the full playbook.

SEO is the familiar work of ranking product pages on Google through keywords, backlinks and optimized descriptions, which they treat as a necessary foundation rather than a complete plan.

• AEO covers AI Overviews: when Google answers a question at the top of the results page, a brand absent from that answer is effectively invisible, even if it ranks third.
• GEO is the newest layer: getting an AI assistant such as ChatGPT to recommend a product by name when a shopper asks for a recommendation rather than a list of links.
• The post gives a tactic for each layer: keep optimizing product pages and site speed for SEO, build FAQ pages, comparison guides and 'best X for Y' articles for AEO, and earn mentions in reviews, Reddit threads, niche publications and expert roundups for GEO.

Reframes search as three ordered jobs, each with its own practical to-do list, rather than a single ranking problem.

For brands planning a campaign

For a DTC brand that wants to appear in AI answers, Patel is a fit for a talk that walks through all three layers and assigns concrete work to each: product-page and speed fixes, question-answering content, and off-site mentions that AI systems draw on. Ask them to show which layer a given brand is currently missing.

Examples: Source 1

Instead of moving straight from hearing about a brand to searching for it, buyers now use AI to synthesize and validate their options first.

• They call the metric for that middle stage 'Share of Model': if a brand is absent from what the model says, it is unlikely to win the impressions and clicks that follow.
• They also treat reviews and customer retention as fuel that trains AI systems to recommend a brand, describing the whole loop as the blueprint for 2026 and beyond.
• The argument applies across ecommerce, media, fintech, pharma and B2B SaaS, not just one sector.

Built a diagram of the revised funnel with an AI image model (Google Gemini 3 Pro / Imagen 3), then labeled the stages and metrics in the post.

For brands planning a campaign

Useful for a brand that wants to treat its AI visibility as a measurement problem, not just a content problem. A session or engagement could define what 'share of model' means for the brand's category, then map the reviews, retention signals and third-party sources an AI system would read before recommending it.

Examples: Source 1

A brand writes its process once in a simple markdown file: brand guidelines, brief format and copy instructions.

• Claude then reads that file automatically when the matching task appears, and separate Skills for research, briefs and copy chain together in one conversation instead of living in disconnected projects.
• The stated audience is ecommerce brands and agencies already using Claude for creative work but losing time on repeated setup.
• The post lists the accompanying playbook's contents, including the architecture for how Skills trigger and compose, five ready-to-use templates for DTC and agency teams, step-by-step setup, and guidance on writing instructions that produce consistent output.

Patel explains a technical process by naming the specific failure it removes (about twenty minutes of setup per conversation) and describing the file, trigger behavior and templates readers need to reproduce it.

Access to the playbook is gated: readers are asked to like the post and comment 'SKILLS' to receive it by direct message; the post notes that readers must be following the account.

For brands planning a campaign

A strong topic for a hands-on workshop with a creative or content team, since the material focuses on changing day-to-day processes rather than campaign ideas.

• When briefing Patel, ask for the worked setup rather than an overview: what the Skill files contain, how the chain is structured, and how quality is kept consistent across a team.
• Patel distributes these materials through comments and DMs, so clarify how a gated asset will be handled in any sponsored arrangement.

Examples: Source 1

Headings make the framework scannable, and the practical content is usually a short list of actions tied to each stage.

• Patel also uses free playbooks as a closer, asking readers to like and comment a keyword to receive the material by direct message, as in the Claude Skills post.
• The posts lean on everyday examples a brand would recognize, such as someone asking ChatGPT for a moisturizer for dry skin or searching for running shoes for flat feet.

For brands planning a campaign

The framework-first format adapts easily to a sponsored talk or co-produced article: a brand can supply the category and audience, and Patel's structure turns them into a layered model with clear actions at each step. If the deliverable is a playbook or checklist, plan for comment-then-DM distribution rather than a public download.

Examples: Source 1 · Source 2 · Source 3

The post presents this as using a tool to produce a visual, not as a partnership. It does not state whether sponsorship, payment or a client relationship was involved; ask for clarification.

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The post does not state whether the offer involves sponsorship, an affiliate arrangement or a client relationship with Anthropic; ask for clarification.

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Mohsin Nisar M. also treats AI as a reputation engine rather than a keyword problem, close to Patel's argument that brands must be present in what a model says.

Mohsin Nisar M. builds the case through category examples and third-party research from Semrush and Backlinko, while Patel organizes the same territory into named layers and metrics such as SEO, AEO, GEO and share of model. Their posts tend to end with a practical playbook or checklist; Mohsin Nisar M.'s stay closer to diagnosis and positioning.

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Nativ Yanko's Claude Skills post covers the same problem Patel describes: creative teams lose time re-explaining brand context, and packaged AI instructions can eliminate that repeated setup.

Nativ Yanko focuses on performance ad creative and new-customer acquisition, including ad account research, unique mechanism arguments and NCPA. Patel focuses on search and AI recommendation visibility, with creative process as one process improvement rather than the core commercial pitch.

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Azhan shafique also contrasts AI-assisted research with experienced judgment, overlapping with Patel's interest in making AI output useful and dependable.

Azhan shafique teaches through client stories, such as a surgical instrument manufacturer whose authority AI was not reading correctly, and warns about AI-generated keyword lists that ignore difficulty and intent. Patel uses numbered frameworks and layered models, and is more likely to package the advice as a playbook.

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A DTC brand wanting to know how product data, entity attributes, query intent, schema, reviews and internal linking can be made understandable to AI systems gets a closer match here than from Patel's search-visibility posts.

• Mohsin's posts give a product-page optimization framework and a reputation-engine argument, with category examples such as Amazon, Reddit and DocuSign.
• It is stronger for a team that needs page-level checks before it briefs a talk or consulting session.

The tradeoff is less support for internal AI-assisted content operations.

• The posts cover strategy and a checklist offer, not the delivered playbook.
• Before booking, ask for campaign results, fees, audience roles and facilitation experience.
• Brands whose main need is reusable prompts and creative operations would still need Patel or another content-operations specialist.

Examples: Source 1 · Source 2

The posts describe a Claude Skills setup for DTC creative teams, covering top-of-funnel scripts, hook generation, briefs pushed into Notion, organic concept translation and static ad copy.

• They also make a new-customer acquisition argument built around NCPA rather than blended ROAS, with a collagen-brand example.
• For a campaign need involving creative operations help or a performance-creative session, this is closer to the operational task than Patel's search-focused framework.

The tradeoff is less direct information on SEO, AEO and GEO for ecommerce search visibility.

• The posts cover paid creative strategy and a gated skills playbook, not a search audit or an AI-answer visibility method.
• If search visibility is the core deliverable, pair Nativ with an SEO/GEO specialist or choose one instead.
• Before booking, confirm the posts' claims about agency scale and client results.

Examples: Source 1 · Source 2

The surgical-instrument example shows a practical sequence: improving who-and-what content around the company, reconnecting official pages, and then checking AI search again. The keyword-research post is also a useful caution for DTC teams tempted to let an AI tool generate a high-volume keyword list without difficulty or intent checks.

The tradeoff is narrower DTC ecommerce and content-operations information.

• The examples come from a surgical instrument manufacturer and a new-site SEO context, not a consumer product catalog.
• Ask for a reusable Claude or creative-operations playbook before booking.
• A DTC brand wanting shopper-facing product-page content, AI-assisted creative briefs or a talk on internal content operations would get less from this material.

Examples: Source 1 · Source 2