AEO & GEO Report: Answer Engine and Generative Engine Optimization

Summary

Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) are evolving practices that extend traditional SEO into the age of AI-driven search. In AEO/GEO, the goal is not merely to rank a web page, but to have your content selected and cited by AI answer systems (ChatGPT, Google's AI Overviews, Bing Copilot, etc.) as direct answers to user queries. Unlike classic SEO—which targets page-level ranking signals (keywords, backlinks, etc.) for users browsing search results—AEO/GEO focuses on content clarity, factual accuracy, and structure so that Large Language Models (LLMs) can retrieve, synthesize, and cite it in their responses.

AI search engines use a retrieval-augmented generation (RAG) approach: they index and embed web content into vector databases, then at query time retrieve the most relevant "chunks" of text and prompt an LLM to generate an answer. These answers often come with source attributions (as seen in chat interfaces) or summary lists, and they heavily favor authoritative, up-to-date sources.

In practice this means marketers must emphasize deep, trustworthy content (E‑E‑A‑T: Experience, Expertise, Authoritativeness, Trustworthiness) with clear, self-contained answer segments, use structured data markup, and organize content around user questions.

Key takeaways:

• The top-priority tactics include providing concise answer summaries, structuring content with clear headings and lists, covering topics in depth (topic clusters), and using schema markup and FAQs to aid AI comprehension.
• Technical factors (site speed, crawlability, API endpoints) remain important for crawl access, but AI visibility hinges more on content organization and authority.
• Measurement shifts toward "AI citation rate" and zero-click visibility: track how often AI bots source your content (tools like Conductor, Bluefish AI, or custom tests) and watch indirect impact on brand searches.
• In the SaaS/tech sector (APIs, devtools, AI platforms), focus on developer and B2B intent: create comprehensive tutorials, API docs, and technical guides that answer concrete "how-to" questions.
• Use Q&A formats (e.g. FAQ pages, forum-style sections) and publish original data (benchmarks, case studies) to boost credibility.
• Avoid myths like "SEO is dead" – SEO best practices still underpin visibility. Instead, integrate AEO/GEO techniques into your SEO strategy.
• Over the next 2–3 years, expect AI answers to become the default on many queries, further reducing clicks to traditional sites. Brands that adapt now will maintain visibility as search shifts from links to answers; those that don't may see substantial traffic erosion.

Defining AEO vs. GEO vs. SEO

Traditional SEO (Search Engine Optimization) is the practice of improving a website's visibility in organic search results (e.g. Google, Bing) by optimizing for ranking factors like keywords, backlinks, and technical site health. Moz summarizes SEO as "practices designed to improve the appearance and positioning of web pages in organic search results," with the primary goal of driving clicks and traffic to your site.

By contrast, Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) refer to optimizing content for AI-powered answer systems. AEO "tailors content to the conversational-style interfaces of AI search tools such as ChatGPT, Perplexity and Google's AI Overviews," with the aim "to have content show up in the answer these tools provide rather than [just] rank a web URL in a traditional search engine query." In other words, AEO means structuring and phrasing your content so that AI bots can directly use it as part of their answer, increasing the chances of being cited.

GEO is a closely related term (sometimes used interchangeably with AEO) that emphasizes the rise of generative AI models. Conductor defines GEO as "strategically creating and refining your website content so that answer engines and AI chatbots can effectively understand, surface, and present it to users," noting it's "a natural evolution of SEO" given users get more direct AI answers. A Wikipedia summary explains that GEO focuses on influencing how large language models (LLMs) retrieve, summarize, and present information.

The core idea in both AEO and GEO is the same: your content should be designed as source material for LLM answers, rather than solely as web pages in a search results list.

Key distinctions (adapted from industry sources)

Importantly, AEO/GEO isn't an either/or with SEO but an extension of it. Content that ranks well in search can also be optimized for AI. As Conductor notes, GEO and SEO "are not necessarily mutually exclusive and will likely become increasingly intertwined as search engines integrate more generative AI features." The fundamentals of quality content (E-E-A-T signals, relevance) still apply, but AEO/GEO adds extra requirements around format and retrieval.

How AI Search Engines Retrieve, Cite, and Synthesize Content

Modern AI-powered search systems typically use a retrieval-augmented generation (RAG) pipeline.

1. Crawl & index – They crawl and index a vast corpus of web content, converting pages or passages into vector embeddings.
2. Query time – The system parses the user's natural-language question and uses semantic search over these embeddings to retrieve the most relevant text snippets.
3. Synthesis – These passages are then fed as context into a large language model, which synthesizes an answer.
4. Output – The AI provides the answer to the user along with citations or source links for transparency.

The retrieval step is key: unlike classic SEO which uses page-level ranking algorithms, AI search "break[s] content into passages or chunks and retrieve[s] the most relevant segments for synthesis." For example, Google's AI Mode might decompose a complex query into sub-questions ("fan-out"), retrieve answers for each facet, then combine them. This means that individual sections or lists in your content can be pulled independently. Structuring your content so each section answers a specific question (with clear headings) makes it easier for the AI to extract those chunks.

In the synthesis step, the LLM generates a coherent response from the retrieved snippets. Crucially, many AI systems (ChatGPT with browsing, Google AI Overviews, Perplexity, Bing Chat) list the sources used. For example, a query for "best hot dog joints in Chicago" might yield a bulleted answer with inline citations. The AI combined information from multiple sites (Yelp, Wikipedia, foodie blogs) and shows them as references. In this example and others, you can see that answer engines cite the exact phrases or facts lifted from trusted sources. They also prefer authoritative, current sources. SurferSEO notes that "AI search engines scrape massive datasets…and form answers by citing and compiling information from [the] most relevant sources," often taking advantage of RAG to use up-to-date content. If a source is deemed high-authority (e.g. an industry site, official docs, or major news outlet), it's more likely to be selected.

Academic audits of AI search behavior confirm this bias: generative engines tend to draw heavily from news and media outlets and exhibit commercial/geographic bias in what they cite. In practice, this means brands need to build or leverage authority signals (mentions on other credible sites, strong domain metrics) if they want to be included. As one analysis puts it, "premium editorial placements and digital PR" on credible platforms can serve as authority signals that raise the chance of being cited by LLM answers.

Because AI answers now often replace the need to click to a site (a phenomenon called "zero-click search"), the way we measure success shifts. Traditional metrics like SERP rank are still useful, but new metrics gain importance—e.g. "AI Citation Rate" (how often an LLM references your site in answers) or your "Answer Engine Optimization Score". Tracking these may involve using AI-monitoring tools or custom testing: query popular AI bots with key prompts and see if your content shows up. The WordPress VIP guide recommends using analytics to see what topics are already driving AI-driven sessions and then optimizing around those prompts.

In summary: AI search systems retrieve relevant passages (via embeddings/RAG), synthesize a direct answer, and display or cite the sources they used. For content creators, this means optimizing not just for ranking algorithms but for retrievability and clarity of passages. Writing concise summaries, using Q&A format, and marking up data all make it easier for an LLM to find and cite your content.

Tactical Strategies (Ranked by Impact)

Based on current evidence, the following tactics are among the most impactful for improving visibility in AI-driven search. They are ordered roughly from highest to moderate impact, with SaaS/tech context in mind.

1. Authoritative, Comprehensive Content (High Impact)

Focus on E‑E‑A‑T (Experience, Expertise, Authoritativeness, Trustworthiness). AI systems favor trusted sources, so create in-depth, factual content that establishes your brand's expertise. For SaaS, this means detailed technical guides, data-driven case studies, benchmark reports, or original research. Cite reputable data (even external studies) in your own pages. WP VIP advises demonstrating authority through unique data and research, as "LLMs determine credibility on a search-by-search basis." Similarly, Conductor notes that content must be "factual" and "unique" to stand out in AI results.

Example: A developer blog post on "scaling containerized apps" could include real-world performance numbers or survey results, making it a source the AI is likely to trust.

2. Concise Answer Summaries (High Impact)

Place clear, direct answers at the top of pages or sections. SurferSEO finds that AI answer snippets often come from the first sentences of a section. Write an answer in 1–2 sentences at the very start of each topic or H2, then expand below. Use active voice and lead with subjects (e.g. "You can export data by…"). The goal is that an AI can grab that lead sentence as-is for an answer. This is similar to writing a good featured snippet. Provide a brief TL;DR or bullet summary at the top of articles: Google AI Overviews frequently pull such passages. For SaaS content (like API tutorials), start with "To call our API endpoint, send a GET request to X with these parameters…" before diving into details.

3. Structured Q&A Format (High Impact)

Use question-style headings and FAQ sections. AI chatbots often answer direct questions, so phrasing section titles as questions helps. Surfer found that pages in AI results perform better if they define terms and answer them explicitly. WordPress VIP similarly recommends writing content around "common questions your target audience is likely posing to tools like ChatGPT," and even including FAQs. For example, use an H2 like "How do I authenticate with the API?" followed by a straightforward answer paragraph. FAQ schema (JSON-LD) under each Q&A can further highlight this. This Q&A style also dovetails with voice assistants, where users naturally ask full questions.

4. Structured Data & Schema (High Impact)

Implement JSON-LD schema markup for relevant content (articles, FAQs, products, how-tos, etc.). Google and AI search can use schema to better understand page context. For instance, mark up each FAQ item with FAQPage schema, or use HowTo schema for step-by-step guides. Conductor notes that AI systems "love structured data" and use it to interpret content purpose. SurferSEO's tests show AI Overviews pulled info from FAQs whose text matched the schema exactly. For SaaS sites, add schema for technical documentation pages or knowledge base articles where appropriate. Always ensure the visible text matches the schema to build trust. Validate your schema with tools like Google's Rich Results Test.

5. Clear, Scannable Formatting (Medium–High Impact)

Break content into bullet lists, tables, and short paragraphs. The AI tends to prefer list formats for clarity. Surfer found that 78% of AI answers use lists, so if your content is already in list form, it's easier for the model to extract. Keep paragraphs to 2–4 sentences and sentences under ~20 words. Use subheadings generously (H2, H3) as "signposts"—Conductor notes that AI answers are more likely to pick up content with well-structured headings. For example, the Grammarly blog ("How to Brainstorm") organizes its post so that each subheading directly answers part of the main question. In SaaS docs, use bullet steps or numbered instructions for how-tos.

6. Semantic Depth & Topic Clusters (Medium–High Impact)

Cover topics comprehensively. AI engines may break a query into subtopics ("query fan-out") and pull answers from multiple pages. Having a broad set of related pages on your domain signals authority. Group related content into clusters: a pillar page with high-level overview and sub-pages on subtopics. Conductor emphasizes "semantic consistency"—using related terms and covering all facets of a theme. For example, a pillar on "Cloud security" with clusters on "network security," "data encryption," etc., helps AI understand you as an expert. Use consistent terminology (entity-based writing) so that the model recognizes your scope.

7. Timeliness & Updates (Medium Impact)

AI answers favor up-to-date information. WP VIP notes that while SEO could tolerate some lag, "updates [are] critical, as AI may use cached data." Ensure your content is current: update date stamps, re-run freshness cycles, and add new insights. If your content is outdated (e.g. a 2019 post on technology), it may be skipped. For SaaS (rapid field), even small version changes or pricing updates should be reflected. This can also include publishing time-sensitive content (e.g. "Top DevOps trends 2025") to capture current queries.

8. Multi-Modal Content (Experimental / Future-leaning)

Start incorporating images, charts, code snippets, or video content where relevant. Some AI models are increasingly multi-modal and can ingest images or even code. Tryprofound notes AI is "retrieving and synthesizing multimodal content." For example, adding annotated diagrams or tables with clear labels (and alt-text) may give you extra "slots" in AI answers. For developer audiences, embedding code examples in markdown might get picked up. This is more speculative, but aligning with AI's capacity for images or visual data could pay off.

9. Text Fragment Identifiers (Low / Experimental)

New URL fragments (text anchors) link directly to sections of pages. Surfer suggests these might help AI find exact answers. For now, this is a low-risk experiment: ensure your CMS generates anchors for headings so that AI citations might one day use them. The benefit is mainly improved user experience now (landing on the relevant section via URL).

10. Earned Media & Backlink Strategy (Supporting Impact)

While traditional backlinks matter less for the AI's extraction process, they still build overall authority. As WordPress VIP notes, "providers looking to grow…need to reinvest in fundamentals – developing their reputation…prior to purchase." In practice, PR and partnerships still help your brand appear in AI answers: if reputable sites cite your content, AI will indirectly see you as credible. Being covered in industry publications (TechCrunch, trade journals) can create secondary citations in LLMs.

In summary: Prioritize creating content that answers specific questions with authority and clarity. Use conversational, question-based headings and lists, back it with data, and mark it up so that AI systems can parse it easily. These tactics work together: a deeply authoritative page with clear structure and schema is the most likely to be retrieved and cited by an AI answer engine.

Technical Implementation Checklist

Ensure your technical infrastructure and markup support AEO/GEO efforts:

• Schema Markup – Implement relevant JSON-LD schema across key pages. Include Article, FAQPage, HowTo, Product, and Organization schemas where applicable. For software/API companies, consider SoftwareApplication or TechArticle schemas. Validate with Google's Rich Results Test to ensure correctness.
• Site Accessibility – Keep content crawlable by search bots and available to AIs. Do not block Googlebot or Bingbot; allow AI tools to reach your content. Avoid excessive login walls on high-value info—WP VIP cautions that gating content might prevent LLMs from accessing it. If gated content is needed, ensure other free resources cover the same info.
• Headings and Anchor Links – Use a clear heading hierarchy (H1, H2, H3, …) so AI can segment topics. Add a Table of Contents or anchor links for long pages to help both users and AI navigation. (This can also support text fragments.)
• Structured Data Validation – Regularly run structured data through validators (Google's Rich Results Test, Schema.org validator) to catch errors.
• XML Sitemap and Robots.txt – Keep your sitemap updated to surface new AI-targeted content. Ensure robots.txt doesn't accidentally block new interactive sections (APIs, docs).
• Performance and Mobile – While not specific to AEO, ensure site speed and mobile-friendliness remain top-notch. Slow or broken pages may be ignored by both users and bots.
• API / Integration (advanced) – For large SaaS, consider offering an official GPT plugin or API access so AI tools can query your system directly. If AI can fetch data from your API or knowledge base (like a ChatGPT plugin does), you effectively "optimize" content to be deliverable through the AI.
• Logging & Analytics Hooks – Implement tracking to detect AI bot traffic (e.g. in server logs or analytics). Monitor unusual query patterns (e.g. branded keyword surges without SERP clicks). Use integrated tools (e.g. Parse.ly or Conductor's AI reports) to unify search analytics.
• Embedding & Multimedia – Embed images and videos properly with alt text. If using data charts, include HTML tables alongside images so AI can parse the data.
• Security & Privacy – As AI increasingly interacts with your site, ensure compliance (cookies, etc.) and monitor for any content scraping or misuses of your content.

Content Strategy Framework (SaaS / Tech Focus)

For a SaaS or developer-oriented business, align your content strategy with AI-driven search by:

• Mapping User Queries – Identify the complex, long-tail queries your customers ask (especially in technical, conversational form). WP VIP's example shows how a SaaS query might include ICP details ("mid-sized family law firm, not tech-savvy, budget <$100, free trial, US-based"). Use these to guide content topics. Tools: analyze support tickets, developer forums (StackOverflow, Reddit), AI chatbot logs if available.
• Persona-Driven Content – Craft content for specific personas and use cases (e.g. "How to use our API to integrate X in Y scenario"). Write it as if answering an actual question from that persona, with the criteria they care about (budget, expertise level, region, etc.).
• Content Types – Maintain a mix: Technical Guides (how-to tutorials, API docs, code samples), Explainers (conceptual articles, e.g. "What is X technology?"), Case Studies/Benchmarks (to show authority and provide original data), and Q&A/FAQ sections (each addressing a specific query). For developer-focused SaaS, comprehensive documentation is crucial—ensure it's indexed and well-structured with tables of contents.
• Topic Clusters & Pillars – Build pillar content around core themes (e.g. "API Security") and cluster articles on subtopics ("OAuth 2.0 flows", "JWT best practices"). This semantic organization signals depth to AI. Link clusters internally to reinforce topical relevance.
• Conversational Tone – While maintaining professionalism, use a clear, conversational style in answer sections. AI queries are often phrased naturally, so mimic that style in question headings and answers.
• Highlight E-E-A-T – Add author bios with credentials on technical pages. Mention real-world experience ("Written by a developer with 10 years of experience in X"). Where possible, cite third-party sources or link to studies. WP VIP advises including background (education, career) to meet Google's E-E-A-T guidelines.
• Repurpose and Amplify – Turn blog posts into transcripts or FAQs, generate short explainer videos, or create downloadable assets (whitepapers, templates). AI models love structured lists, so an infographic with clear points (and alt-text) can be parsed.
• Community Engagement – Encourage discussions on forums like StackOverflow or GitHub Discussions, and answer questions on Quora/Reddit. AI crawlers index these—your answers there can lead back to your site and establish authority.
• Localization & Language – If relevant, produce content in languages of key markets, as AI answers will match user language. Also tag content by region (schema or hreflang) to help AI geo-contextualize answers.
• Regular Audits – Periodically review top-performing SEO pages and adapt them for AI. Look at GSC trends for queries that might trigger AI answers and optimize those pages' intros for direct answers.

Measurement and Analytics

Measuring AEO/GEO success requires adapting traditional analytics and using new tools:

• Monitor AI-Driven Traffic – While AI answers often create "no-click" scenarios, some still drive traffic (e.g. AI chatbots sometimes include links the user can click). Use Google Analytics or your web platform to tag traffic from AI channels if possible (some generate referrers like bingpreview or bingbot). Look for spikes in branded or product searches coinciding with AI answer releases.
• AI Citation Tracking – Employ specialized tools (Conductor's AI Visibility, Bluefish AI, RankScience AI) that test queries on various LLMs and report if/when your site is cited. Alternatively, manually test queries in ChatGPT, Copilot, Perplexity, Gemini, etc., and note whether your content appears or is listed as a source.
• Zero-Click Metrics – Keep an eye on zero-click rates via Search Console insights or third-party zero-click studies. If more of your key queries result in featured answers or overviews, expect organic traffic to those pages to dip. That's not necessarily bad if branded or downstream actions compensate.
• Engagement & Conversion – Track on-site behaviors from AI-originating visits. Surfer noted that AI-driven visitors can convert at 23× the rate of organic SEO visitors (Ahrefs data), suggesting AI traffic is high-intent. Measure conversions (sign-ups, downloads) per channel; AI-driven leads may be fewer but more qualified.
• Content Performance – Use A/B testing on headlines or answers by slightly modifying intro summaries, and see if AI citation frequency changes over time.
• Brand Lift – Monitor brand awareness in surveys or social listening. Increased mentions of your brand in contexts like "ChatGPT says [brand]" or on Q&A sites can indicate rising AI visibility.
• SEO vs AEO KPIs – Maintain traditional KPIs (rankings, organic traffic) but add new ones: AI Citation Rate (times cited by AI answers) and Zero-Click Answer Share (percent of queries answered without a click in your niche). Some enterprises build dashboards combining both SEO and AI metrics.

Risks, Myths, and Common Mistakes

• Myth: "SEO is dead with AI." False. SEO fundamentals (quality content, good UX, links) remain crucial. Google still controls ~90% of search, and even Google's AI features still rely on SEO-optimized pages for sourcing. WP VIP notes that Google said "SEO will get sites ranked in AI overviews."
• Mistake: Over-optimizing for ChatGPT only. Don't write content just to be scraped by AI at the expense of human readers. AI algorithms will evolve, so focus on clear, helpful answers that satisfy people too. A balanced approach is safer: many SEO pros agree "provide the most relevant, authoritative answers" and you'll be visible everywhere.
• Myth: AI can replace content creators. Be cautious of generating low-quality AI content hoping to game AEO. AI bots discriminate against vague or unsubstantiated text. Google updated E-E-A-T guidelines to penalize "AI slop." Content should still be human-reviewed and fact-checked.
• Risk: Outdated or Inconsistent Schema. Mismatches between visible content and structured data can backfire. Surfer warns that inconsistency is a trust signal for bots. Always keep your on-page text and schema in sync.
• Mistake: Keyword-stuffing Q headings. Simply prefixing every header with a question keyword is insufficient. The answer beneath must genuinely address that question concisely. WordPress VIP says your summaries must be "LLM-scrapable"—i.e. useful and factual.
• Risk: Content Gatekeeping. Hiding crucial content behind logins or interactive apps means AI can't cite it. If you must gate (e.g. whitepapers), ensure at least summaries or key data are public. Otherwise, "lower the gates" on content you want AI to cite.
• Myth: Only big brands benefit. While large publishers have an edge (Pressmaster notes publishers striking deals for AI traffic), niche SaaS can still win with smart AEO. Academic research shows AI engines often cite specialized blogs and forums too. Even small sites should build expertise in their narrow domain; AI can surface any source that meets quality criteria.
• Risk: Legal/Copyright Issues. Keep an eye on evolving copyright law. If you contribute content, you may lose some traffic (as AI answers may use it without click), but also may need to negotiate attribution deals eventually (like news publishers are doing). Always ensure your content licensing or fair-use stance is clear.
• Mistake: Ignoring Analytics. If you "optimize for AI" but never check if it's working, you're flying blind. Use analytics to see how AI trends affect your site, and be prepared to pivot.

Predictions (Next 2–3 Years)

• Continued Growth of AI Search Adoption – More users will default to AI answers. Surfer cites that 15 million US adults already use AI as a primary search method, with usage expected to triple by 2028. Bing, Google, and others will roll out AI chat features in more markets. As a result, the share of searches ending without a click may climb beyond 60%, solidifying zero-click as the norm for many queries.
• SEO and AEO Merge – The distinction between SEO and AEO/GEO will blur. Search engines themselves (Google's SGE, Bing Chat, etc.) will increasingly integrate both traditional and generative modes. Marketers may stop differentiating "SEO vs AEO" and treat them as one holistic strategy of "search everywhere optimization." Tools and platforms (like Conductor, Ahrefs) will build unified dashboards for organic and AI-driven visibility.
• New Platforms & Modalities – AI assistants will expand beyond text. Expect more voice-based AI answers (smart speakers), and possibly VR/AR interfaces where AI narrates or highlights info. Also, specialized AI search verticals (code search, academic search, image search) will emerge. SaaS companies should watch for domain-specific AI tools (e.g. "Developer Copilot" integrated in IDEs) and adapt content (e.g. code snippets that LLMs can ingest).
• Emphasis on Trust and Fact-Checking – As hallucination fears grow, there will be greater emphasis on verifiable content. Content with clear citations, author credentials, and fact-checking will rank higher in AI outputs. Blockchain-like source tracking or digital watermarks could evolve to prove authenticity. Regulatory pressure (e.g. EU AI Act) might require AI answers to provide source transparency, further increasing the value of official citations.
• Metrics and Standards – Just as SEO has standardized metrics (CTR, impressions), expect new industry standards for AI visibility. Gartner/Forrester may define "AI visibility score," and search consoles (e.g. Google) might offer "AI answer impressions" reports. Analytics will evolve to attribute conversions to AI answers, and marketing teams will budget for "AEO" much like they do for SEO today.
• Hybrid Search Experiences – Search results pages will blend AI answers with traditional links. For example, Google might show an AI overview plus a list of sources. Users may get the best of both: a quick answer and the option to click deeper. This means even in a "generative" world, having great web pages will still matter for the links on that page.
• Competitive Differentiation – Early adopters of AEO/GEO (especially in SaaS/tech) will see brand lift. As Pressmaster notes, companies that "master GEO early will gain significant advantages" like brand amplification and authority positioning. We expect industry leaders to tout their AI rankings, and laggards to scramble. In the tech sector, those with substantial documentation and developer communities (e.g. Atlassian, Stripe, AWS) are likely to dominate AI citations unless others catch up.

Overall, the next few years will see AEO/GEO move from "hype" to expected practice. The AI-driven search landscape is still coalescing, but the trend is clear: optimize for answers as much as for keywords. Tech companies that build deep, clear, and trustable content will be the ones whose brands light up AI-generated answers.

Citations

• Answer Engine Optimization (AEO) | WordPress VIP
• What is GEO? Generative Engine Optimization Explained | Conductor
• What is Answer Engine Optimization? 7 AEO Strategies for 2025 | SurferSEO
• Generative engine optimization | Wikipedia
• As AI Use Soars, Companies Shift From SEO To GEO | Pressmaster
• Nearly 60% of Google searches end without a click in 2024 | Search Engine Land
• Answer Engine Optimization Vs SEO: What's The Real Difference? | Revved Digital
• AEO vs. GEO: Why they're the same thing (and why we prefer AEO) | Tryprofound

All sources (by domain)

• wpvip – WordPress VIP
• conductor – Conductor
• surferseo – SurferSEO
• en.wikipedia – Wikipedia
• pressmaster – Pressmaster
• searchengineland – Search Engine Land
• revved – Revved Digital
• tryprofound – Tryprofound

https://chatgpt.com/g/g-p-697b566d0e8c8191995a32fcbc680450-obsurfable/c/698a0f9b-a1c8-832f-a12a-7ec18bae55d3

Got a really good deep research here. I should add references to Obsurfable, Circuit, In Plain English where possible https://chatgpt.com/share/69851bf4-fe38-8000-96aa-2c0fb1cb0901 - this can be posted in lots of places and can be split up into posts, comments, etc. time to position myself as an expert in this space