Category mistake
AEO/GEO is not "SEO, but for ChatGPT"
The tempting move is to put AEO and GEO into the same mental bucket as "blog SEO." You pick keywords, write pages, add schema, and wait. Some of that still matters. The problem is that AI Search does not only retrieve a page. It assembles an answer from many source surfaces: owned pages, third-party mentions, profiles, documentation, public evidence, reviews, partner pages, and the model's own learned priors.
That makes AEO/GEO less like buying media and more like running a weekly visibility workflow. The team has to ask: what do answer engines currently say about us, which sources do they trust, which claims are unsupported, which competitor claims are easier to cite, and which owned pages are clear enough to become answer units?
If this is managed as a channel, the output becomes "more articles." If it is managed as a workflow, the output becomes a source-backed market position that can survive retrieval, summarization, and citation.
Operating loop
The workflow has six recurring jobs
In our work at Humanswith.ai, the first job for the Agentic Workspace is AEO/GEO because it is measurable, cross-functional, and painful enough to reveal the limits of raw AI tools. The loop is simple on paper and demanding in practice.
| Job | Question | Human gate |
|---|---|---|
| Measure | What do ChatGPT, Claude, Perplexity, Gemini, Google AI features, and Yandex/Neuro-style answers say today? | Approve the prompt set and the visibility scorecard. |
| Diagnose | Which answer claims, missing facts, weak pages, and third-party source gaps explain the result? | Decide the one next business claim worth improving. |
| Produce | Which canonical page, research note, service page, case, FAQ, or profile update gives AI systems a better source? | Review claims, byline, evidence, and commercial intent. |
| Optimize | Is the page crawlable, internally linked, schema-consistent, answer-first, and useful to humans? | Approve technical QA before publishing. |
| Distribute | Which external adaptations, profiles, communities, and partner surfaces should link back to the canonical source? | Prevent duplicate-canonical confusion and low-quality blast publishing. |
| Verify | Did prompts, citations, traffic, assisted leads, or sales conversations change after publication? | Choose whether to iterate, expand, or stop. |
Why tools disappoint
A tracker is not enough
AI visibility trackers are useful. They show the gap. They do not close it. The same is true of keyword tools, content generators, and schema checkers. Each tool sees one slice of the problem, while the business problem crosses strategy, content, website architecture, legal/commercial claims, and distribution.
This is why teams get stuck after the first audit. They know a competitor appears more often. They know their own service page is vague. They know a few citations come from weak surfaces. But the next step is not obvious because the work is not one task. It is a chain of tasks that needs memory, source control, approval, and proof.
The better pattern is a workspace-level workflow: the audit creates a work packet, the content agent drafts source-backed changes, the website agent checks crawl and schema, the publisher agent keeps canonical rules clean, and the proof loop measures what changed.
Agentic Workspace
Why AEO/GEO wants an agentic workspace
Raw chat is good for exploration. It is weak as an operating system. A marketing team needs prepared agents that know the brand facts, approved claims, source library, canonical rules, rejected examples, and current funnel priorities. The point is not to let agents "do marketing." The point is to turn repeated marketing work into reviewable packets.
For AEO/GEO, that means every cycle should produce artifacts a human can inspect: prompt captures, cited-source tables, page briefs, draft pages, schema checks, distribution instructions, and a before/after scorecard. This is why I describe AEO/GEO as an early use case for the Humanswith.ai workspace rather than a standalone optimization trick.
The product thesis is narrow on purpose: start with the visible, measurable workflow of AI Search visibility, then expand to the rest of the marketing operating system. If the workspace can coordinate AEO/GEO well, it can later coordinate agents for briefs, landing pages, cases, reporting, outreach, and campaign operations.
Canonical rule
Methodology and commerce should live on different canonical surfaces
This page is a methodology canonical. It belongs here because it explains the founder thesis and the operating model. Commercial service pages, cases, pricing-like offers, and implementation packages should be canonical on Humanswith.ai, where the buyer can move from problem to service to product CTA.
The distribution rule is simple: publish the canonical source first, then adapt elsewhere. VC.ru, Medium, LinkedIn, Habr, and community posts can be useful distribution surfaces, but they should point back to the canonical page. Cross-domain rel=canonical is not a dependable strategy for every platform, so the safer operating rule is visible source attribution and internal discipline.
| Surface | Canonical job | CTA |
|---|---|---|
| gregshevchenko.com/research | Methodology, research, founder POV, byline authority. | Move readers to the Humanswith.ai implementation surface. |
| humanswith.ai | Commercial service pages, platform pages, cases, agent descriptions. | Book, brief, evaluate, or start a scoped implementation. |
| VC.ru / Medium / LinkedIn / Habr | Adaptation and distribution, not the canonical source. | Visible "source" link back to the canonical page. |
Product map
Where this maps to Humanswith.ai
The Russian commercial funnel should be explicit: build a pillar page for "prodvizhenie v neyrosetyakh" and AEO/GEO promotion, support it with cluster posts, migrate the best VC.ru cases into canonical Humanswith.ai case pages, and connect the service layer to the platform layer for Marketing Agents.
The Greg site should support that funnel from one step above commerce: methodology, source discipline, measurement, and operating model. That makes this article a bridge, not a duplicate. It explains why the product exists, then sends the buyer to the product surface.
Commercial next step
For implementation, start with the Humanswith.ai AEO/GEO service and the Marketing Agents platform pages. Those pages should own the commercial canonical, while this page stays the research/byline source.
FAQ
Questions this page should answer
Is AEO/GEO just SEO with new keywords?
No. SEO fundamentals still matter, but AI Search visibility adds prompt measurement, answer composition, citation behavior, source distribution, and post-publication verification.
Should every AEO/GEO article be canonical on Greg's site?
No. Methodology and founder POV belong here. Commercial service pages, cases, and implementation offers should be canonical on Humanswith.ai.
Where should VC.ru and Medium fit?
Use them as adaptations and distribution. Publish the canonical source first, then adapt with a visible source link back to the canonical page.
Why start the workspace product with AEO/GEO?
Because AEO/GEO is cross-functional and measurable: it touches research, content, website QA, distribution, and reporting. That makes it a strong first workflow for marketing agents.
Sources
Source base
AI features and your website. Used for the claim that AI features do not require special technical optimization beyond normal Search fundamentals, crawlability, and helpful content.
Explore information in new ways with generative AI in Search. Used for the shift from result pages toward AI-assisted answer exploration.
Yandex launches Neuro. Used for the model of search plus generative neural networks with source links.
GEO: Generative Engine Optimization. Used for the term GEO and the visibility-in-generative-responses framing.
Citation or Confidence: Source Use in Retrieval-Augmented Generation. Used for the distinction between being selected as a source and being absorbed into an answer.
Structuring Documents to Improve LLM Search. Used for the importance of structure, chunking, and retrievability.
Related research