Generative Engine Optimization, or GEO, is the practice of improving the clarity, accessibility, evidence, and answer fit of information that may shape AI-generated answers. It builds on technical SEO and content quality, but measures an additional outcome: whether AI experiences can identify the brand, use trustworthy information about it, and include it accurately in relevant answers.

GEO cannot guarantee a mention, citation, or recommendation. Platforms control their own models, retrieval systems, interfaces, and policies. A responsible program improves the information environment and measures observable outcomes.

For the strategic foundation, read Why AEO Matters. For UnderAI's model of mentions, citations, framing, and recommendations, read The AI Answer Authority Model.

Phase 1: Diagnose Current AI Visibility

Begin with a controlled baseline. Define the markets, audiences, product categories, and decisions that matter. Build a small priority prompt set and test it across the AI experiences relevant to the audience.

Record:

  • brand mentions and absences;
  • competitors included in the same answers;
  • visible source links;
  • recommendation or comparison context;
  • material framing errors;
  • official pages that appear or fail to appear; and
  • the platform, mode, locale, and time of each observation.

The baseline should lead to a problem statement. “Improve GEO” is too broad. “Our brand is absent from security-validation prompts and our security page is never cited” is actionable.

Use the AI search tracking definition to establish the data model and the monitoring guide to run the test.

Phase 2: Research the Prompt Landscape

Keyword lists are not enough because AI questions often include audience, constraints, comparisons, and decision criteria. Organize prompt research around jobs the user is trying to complete.

Include:

  • category definitions and education;
  • problem diagnosis;
  • alternatives and comparisons;
  • use-case and industry fit;
  • implementation and integration;
  • trust, risk, and compliance validation;
  • pricing or procurement questions only when verified information exists; and
  • branded fact-checking questions.

Map every priority prompt to an intended page or evidence source. If no page can answer the prompt accurately, record a content gap. If the answer requires an unavailable customer fact, record it as missing information instead of inventing it.

Phase 3: Build Answer-Ready Content

Answer-ready content makes a useful claim easy to find, understand, qualify, and verify. It should serve a reader even if no AI system ever cites it.

For each priority page:

  1. State the direct answer near the relevant heading.
  2. Define important terms consistently.
  3. Explain scope, limits, and who the advice applies to.
  4. Use descriptive H2 and H3 headings.
  5. Add examples, comparisons, tables, or procedures where they improve understanding.
  6. Link related concepts with descriptive anchor text.
  7. Cite original or authoritative sources for external claims.
  8. Keep title, description, visible content, and structured data consistent.

Do not publish many near-duplicate pages for minor prompt variations. Build one strong page for one dominant intent, then support it with genuinely distinct cluster pages.

Phase 4: Clarify the Brand Entity

An AI system should encounter a consistent explanation of who the brand is, what it offers, who it serves, and where it operates. Review the homepage, about page, service pages, contact details, author or publisher identity, and structured data for contradictions.

Clarify:

  • legal and public brand names;
  • primary category and services;
  • target customers;
  • service regions and languages;
  • official contact points;
  • product or service relationships; and
  • terms that have a specific internal meaning.

Use Organization, WebSite, Service, Article, BreadcrumbList, FAQPage, or other relevant structured data only when it matches visible content. Schema is a consistency layer, not a substitute for the page.

Phase 5: Strengthen Evidence

Claims become more useful when a reader can evaluate their basis. Inventory the important claims on priority pages and classify their support.

Possible support includes:

  • official product or technical documentation;
  • public methodology;
  • verifiable certifications or registrations;
  • named research sources;
  • public case studies approved for use;
  • clearly attributed customer evidence; and
  • relevant independent references.

Do not create testimonials, awards, certifications, customer totals, or performance results to fill an evidence gap. Remove, qualify, or document unsupported claims through the approval process.

Evidence diversity matters because the official website is only one part of the public information environment. Third-party coverage can help readers verify a claim, but outreach and distribution should follow editorial and platform policies.

Phase 6: Secure the Technical Foundation

GEO does not replace crawlability and indexability. Google states that the same foundational SEO practices apply to AI features in Search and that eligible supporting pages must be indexed and able to show a snippet.

Check:

  • status codes and static route availability;
  • robots access and page-level indexing directives;
  • canonical normalization;
  • internal links to priority pages;
  • unique titles, descriptions, and one H1;
  • mobile accessibility and page experience;
  • visible text rather than image-only claims;
  • structured data that matches the page;
  • sitemap inclusion for organic-growth pages; and
  • stable, descriptive URLs.

Machine-readable support such as llms.txt or a brand-facts file can make the site's own publishing contract clearer, but it should not be presented as a platform requirement or a ranking guarantee.

Phase 7: Publish and Connect the Topic Cluster

Prioritize the smallest set of pages that covers the decision journey without cannibalization. A practical GEO cluster may include:

  • a foundational definition or strategy pillar;
  • a proprietary framework or methodology page;
  • an implementation guide;
  • an operational monitoring guide;
  • a metrics reference; and
  • supporting comparison, FAQ, or case pages when evidence exists.

Link from relevant service pages and hubs into the pillars. Link between cluster pages where the reader's next question changes. Avoid generic anchors such as “click here.”

Phase 8: Monitor, Learn, and Iterate

Repeat the original prompt matrix after publishing. Use the same prompt versions, platform scope, and denominator rules wherever possible.

Review:

  • mention rate;
  • brand citation rate;
  • prompt visibility coverage;
  • share of answer;
  • recommendation frequency;
  • framing accuracy;
  • cited-domain changes;
  • organic traffic and conversions; and
  • qualified pipeline signals where attribution is available.

The GEO measurement guide defines the formulas. Treat visibility movement as evidence for investigation, not automatic proof of causation.

Hypothetical GEO Example

This example is illustrative and does not describe an UnderAI customer.

Northstar Cloud, a fictional B2B software company, wants to be considered for regional data-governance projects. Its baseline shows that the brand appears for branded prompts but is absent from most unbranded shortlist prompts. Several answers also describe its service region incorrectly.

The team maps the affected prompts to three problems:

  1. The service page does not state the eligible regions near its core description.
  2. The technical documentation explains controls but not the buyer questions those controls answer.
  3. The website has no public methodology connecting implementation steps with evidence.

The team clarifies the service scope, creates an answer-ready implementation guide, links the guide from the service page, aligns Organization and Service schema with the visible copy, and adds references for technical claims. It does not create a customer result or certification.

One month later, the team repeats the same prompt matrix. It reports changes in mentions, citations, and framing accuracy, along with platform and sample-size notes. The result is treated as a new observation period, not as proof that one page caused every change.

Common GEO Failure Modes

  • Publishing generic AI-written pages without a distinct purpose.
  • Targeting prompts that do not map to the actual business.
  • Using unsupported superlatives or fabricated evidence.
  • Treating schema or llms.txt as a shortcut around weak visible content.
  • Creating multiple pages with the same search intent.
  • Optimizing only the official site while ignoring public contradictions.
  • Measuring one answer and calling it a rank.
  • Reporting positive mentions without checking factual framing.
  • Promising inclusion in an AI answer.

GEO Implementation Checklist

A complete implementation has a defined audience and prompt landscape, reproducible baseline, clear entity facts, answer-ready priority pages, visible evidence, sound technical SEO, intentional internal links, transparent metrics, and a scheduled review cycle.

The objective is not to manipulate an answer engine. It is to make accurate, useful, and verifiable information easier for people and systems to retrieve and interpret.

References