GEO, AEO, LLMO and AI SEO: assess proposals by deliverables and evidence

GEO, AEO, LLMO and AI SEO are labels a proposal may use. They do not independently specify a product, technology or deliverable. The same label may cover different work, while different labels may overlap. Ask for concrete pages, workflows, settings and evidence before comparing them.

The purpose is deciding whether work addresses a known problem. An acronym does not establish that it replaces SEO, changes model memory or provides a guaranteed answer position.

Common interests and questions to ask

This table organizes proposal questions, not agreed architecture layers or algorithm specifications. If several tasks are bundled, itemize their scope and deliverables instead of buying separate packages solely because their acronyms differ.

Separate editable work from external outcomes

Content teams can correct evidenced company information, offers, sources and applicable conditions. Engineers can assess pages, links, access and agreed settings. These changes can have inspectable records and acceptance methods.

An external product's retrieval, presentation and source selection are not fully controlled by a website team. Google's AI-search guidance treats AEO/GEO as industry terminology while retaining SEO foundations; special schema.org markup is unnecessary. This does not specify every other product's steps.

For structured data, identify types, pages and maintenance responsibility. Google's introduction requires markup to describe visible page content. Markup cannot replace factual corrections or guarantee citations from AI services.

What would establish model-memory changes?

Ask whether observation used search, tools or supplied context. A correct brand description does not prove that particular content changed model parameters. One answer cannot establish behavior across versions or users.

OpenAI's crawler documentation separates search from possible training-data access. Publication, crawler permission or a recorded fetch does not tell a website team when information entered a specific model. If a vendor proposes actual training or fine-tuning of a controllable model, separately identify the model, data rights, assessments, versions and update responsibility.

Compare proposals with a task record

Ask each proposal to specify:

• Problem and scope: Which error or reader obstacle exists? Which pages, languages, products and modes are involved?
• Deliverables: Content corrections, technical changes, observation records or assessments? Who approves and takes over?
• Evidence: Which sources, dates and results are compared? What are the denominators and coverage limits?
• Constraints: Which outcomes are uncontrollable? Which settings require organizational or data-owner approval?
• Costs and continuation: Which tools, review, maintenance and updates are included? When is continuation reconsidered?

Assess reference records, visitor actions and actual outcomes separately. One visibility score cannot replace assessment of the whole task. The existing SEO and AI-search comparison helps identify mixed measurement claims.

The FDM service concerns collaboration on a defined marketing workflow. Establish data, integration, human review, deliverables and operating responsibility before deciding whether the proposal is worthwhile. Labels can organize discussion; agreed work and evidence support the purchasing decision.