SEO, GEO & AEO: The Workbook for Discoverable B2B Expertise

1. Define three objectives instead of one optimization promise

In this framework, SEO describes organic discoverability in search systems, AEO describes clear answers to specific questions, and GEO describes making credible expertise useful in generative answer situations. These are working terms, not separate technical certifications. Google describes no additional special optimization for its AI search features and does not guarantee inclusion even when requirements are met. The primary source is listed below.

In our fictional case, PrismaOps wants a credible presence around operations software selection. This becomes three checkable objectives: make important pages technically accessible, answer selection questions with supporting evidence and document observed visibility. “Be recommended by every LLM” is not a controllable acceptance criterion. Begin by recording which customer question you want to answer better, which page owns that question and how you will recognize an improvement in its substance.

2. Start with an inventory and a clear job for each page

Export relevant pages and add page type, audience, question, desired action and subject-matter owner. Group pages by their job rather than their URL folder alone. A product page supports an application decision, a glossary explains a term, and a guide teaches a working process. Where two pages answer almost the same question in almost the same way, investigate their independent value.

PrismaOps’s fictional inventory contains a product page, a general blog and three short vendor-selection articles. None explains how to obtain internal approval for a selection. Instead of producing a fourth similar post, the team develops a substantial selection guide and clarifies the other pages’ jobs. The table is a planning example, not a keyword volume model. Priority comes from buying relevance, available expertise and a specific quality gap. Search volume can inform that decision but does not replace it.

3. Test actual access rather than a robots.txt file alone

Test an important page without signing in, using its intended URL and the host that actually serves the publication. Record status code, redirects, access screens and the main content received. Review hosting and security rules as well. A readable local HTML file does not establish that an external request receives the same material. A private preview therefore does not count as publicly crawlable.

OpenAI distinguishes the search crawler OAI-SearchBot from training-related GPTBot; their settings are independent. Its search guidance identifies robots.txt and access from published IP ranges. In fictional PrismaOps’s case, robots.txt permits access but the CDN delivers a verification screen. The finding is “external access blocked,” not “GEO optimized.” Investigating content processing becomes worthwhile only after published access works reproducibly. Record the time and actual response evidence so another reviewer can repeat the finding rather than relying on an undocumented reassurance.

4. Review main content and navigation as a readable document

Check whether a reader can understand the page’s purpose without watching an animation, completing a form or playing an embedded video. Use a text view and examine headings, paragraphs, link descriptions and tables. A collection of keywords is technically readable but does not explain a relationship. The actual value must appear in statements that make sense together.

In the fictional example, PrismaOps discovers a comparison graphic whose criteria exist only inside the image. The team adds a text table describing prerequisites, limitations and decision questions. The graphic remains as an overview. Descriptive navigation links connect the selection guide, integration information and relevant service. Internal links have an editorial reason: They answer a specific next question. An automatically inserted block containing dozens of near-identical anchors is not a substitute for a useful reading path in this framework. Review a complete journey from understanding the problem to an appropriate enquiry.

5. Write answers with conditions and counterexamples

A credible answer gives the decision first, followed by the conditions under which it applies. Add a counterexample so readers can locate their own situation. “Which operations software is best?” requires criteria and a transparent comparison process rather than an unsupported winner. Avoid apparently objective rankings when the products have not been examined systematically.

PrismaOps’s fictional selection guide says: “Start with the process that crosses teams and regularly requires traceable handovers. Examine ownership and existing data before comparing feature lists.” The explanation then covers cases where a simple task list may be sufficient. That boundary reduces overstatement and improves the quality of the decision. Every important answer receives an example, source or reproducible piece of working evidence. Shorten it afterward; an abbreviated claim without a necessary condition may mislead more than a longer explanation. The goal is usable clarity, not the shortest possible passage.

6. Use structured data as a verifiable description

Structured data describes the existing page; it does not replace the content. Google identifies JSON-LD as a practical format and requires alignment with visible information. First decide which entity is genuinely described, then reconcile names, authorship, publication information and image references with the page. Syntactically valid output can still make false statements.

In its fictional audit, PrismaOps finds a supposed expert with an unsupported qualification and a product rating without a visible basis. Both are removed. The team then documents the real responsible person and actual content type. It checks the generated output after a complete build rather than inspecting an isolated code fragment only. Do not invent awards, customer reviews or capabilities to make a larger schema. The acceptance question is whether each material statement can be confirmed on the page or in authorized evidence. Retain a small, accurate description rather than a comprehensive-looking collection of uncertain claims.

7. Maintain a claim and source register

For important statements, record their source, scope and accountable owner. Separate product facts, observed results, assumptions and your own recommendations. An industry study may explain a general relationship without proving your product’s impact. An internal workshop does not establish representative market demand either.

In the fictional case, PrismaOps replaces “halves your coordination effort” with a verifiable account of available capabilities. A future results report would require a period, baseline, measurement method and limitations. Worked examples are explicitly labelled fictional. For platform capabilities, the text refers to responsible primary documentation and records a review date. This is an editorial quality process, not proof that an answer system will cite the page. Its immediate value is identifying outdated or overextended claims in time. Make the owner responsible for updating affected passages when a source or product fact changes.

8. Give images, diagrams and media their own explanatory job

Ask which job every visual performs. A process diagram can explain dependencies, a comparison table can expose differences and a screenshot can substantiate an actual interface. Another decorative image may break up the page without answering an additional question. Plan visual material together with its explanation rather than filling empty space afterward.

PrismaOps creates a fictional approval-path diagram and a table of required evidence. Google recommends contextual image descriptions and discoverable image resources. The team therefore describes the actual image in alternative text, adds an explanatory caption and retains the main point in the prose. A complex diagram receives a longer text alternative. Screenshots use synthetic data; confidential customer information is not published to manufacture authenticity. Before approval, the team checks mobile legibility, available image files and understandable labels together. An attractive diagram that cannot be read at the expected size fails that editorial review.

9. Keep language versions and preferred URLs consistent

Translations need their own subject-matter approval. A German buying term may create a different expectation in English. Review the question, offer, examples, links and call to action in each language. Automatic translation may provide a draft, but does not demonstrate suitable terminology or an accurate service description.

In fictional PrismaOps’s case, German and English remain separate language pages. Google describes canonical signals for preferred URLs and hreflang for language alternatives; those jobs should not be confused. The team checks whether language references connect correctly and preferred URLs point to consistent destinations. It also checks whether language switching opens the corresponding content instead of always returning to the homepage. In the working process, test the actual reader journey before confirming its technical mapping. A formally present language label cannot repair a false or incomplete translation. Keep a bilingual owner for material product and commercial terminology.

10. Measure visibility as an observation with explicit limits

Before changing anything, record the evidence already available: organic entries, relevant enquiries, search topics and conversation clues. For AI-answer checks, capture the question, date, language, system used, visible sources and actual response. A single test is a snapshot. Repeated observations may be described, but without a controlled design they do not establish causal impact.

Fictional PrismaOps checks a small fixed set of buying questions and retains answers with no brand mention. The team separates a cited URL, an unsupported brand mention and an incorrect attribution. It also checks whether organic visitors actually use the new guide or ask relevant questions. A higher proportion within its own small test set is not presented as market-wide “LLM share of voice.” The report always explains the slice examined and the unknown portions. Otherwise a precise-looking percentage can conceal a small, selected and unstable sample.

11. Organize a two-week quality sprint

Days one and two cover inventory, access tests and selection of a small page package. Subject-matter owners then collect sources, product boundaries and real buying decisions. The second half produces revised answers, tables and media. Before publication, the technical output and factual statements receive separate reviews. A short sprint bounds the workload; it does not promise a search impact inside two weeks.

In its fictional sprint, PrismaOps begins with one selection guide and a related product page. An access defect takes priority over additional content. Unsupported benefit figures block editorial approval. A broken language link blocks technical approval for the affected version. Publication follows only after these gates are passed. Every known remaining issue then receives an owner and a further review. This turns “optimized” into an auditable state with limits rather than a blanket label applied to the entire project.

12. Maintain a change log rather than a one-off checklist

After each publication, record which claim, page or technical setting changed and why. Attach the evidence and acceptance review. Changes without a documented reason make later interpretation difficult: You cannot tell whether an effect relates to content, access, demand or another intervention. Keep unrelated changes separate enough to understand them later.

PrismaOps’s fictional operating model schedules periodic review of important platform rules and editorial review when the product changes. An article does not receive a new date merely because it is old. When a capability or source changes, the relevant passage is actually revised. Pages without independent value are consolidated or given a deliberate new purpose. This working paper therefore ends with a maintained register: What do we know, what was tested, what remains unresolved and who owns the next step? That traceability makes your expertise more dependable over time, even when visibility outcomes remain uncertain.

YOUR WORKSHEET

Your SEO, GEO and AEO review register

Replace each task with your finding and an evidence link. The first row is a fictional example. Use open, checked or blocked status values and record technical and editorial approvals separately.