If your company ranks in traditional search but rarely appears in AI answers, do not begin by publishing dozens of articles or installing a file that promises to make the site understandable to models. Select 20 to 40 real commercial questions, preserve a baseline of answers and cited sources, fix technical access, build pages with direct answers and verifiable evidence, make the company entity consistent across the site, and repeat the measurement. The first investment should create an observable loop from question to source, citation, visit, and commercial outcome.
GEO does not replace SEO. Google says existing SEO fundamentals remain relevant to AI Overviews and AI Mode, with no special schema or new machine-readable file required. The operational difference is the unit of work: in addition to queries and rankings, the team tests full buyer questions, examines how the company is described, records which sources support the answer, and checks whether a visit becomes a useful business action.
Start with buyer decisions, not a keyword export
Build a compact question set across three groups. Problem questions describe a recognizable pain. Implementation questions ask what to build, integrate, measure, and operate. Buying questions ask which partner to hire, which scope drives cost, and what evidence to compare. A B2B sequence might move from “how can we reduce campaign rework with AI?” to “who can implement that workflow across our CRM and CMS?” Questions tied to a decision create a clearer commercial content backlog than broad topics.
For each question, record language, market, persona, decision stage, observed answer, named companies, cited sources, date, and model or surface. Answers vary across users, models, and time; one observation is not a rank. Re-test a fixed sample and preserve evidence so the team can distinguish movement from normal variability.
The AI Discovery Evidence Loop
- 1. Question — real demand, context, market, language, and the decision the buyer is trying to make.
- 2. Source — a public page with a direct answer, author, date, method, examples, limitations, and verifiable evidence.
- 3. Entity — consistent company name, services, markets, people, products, and relationships across owned and legitimate external sources.
- 4. Access — crawlability, indexation, canonical, hreflang, internal links, sitemap, rendering, and preview controls working as intended.
- 5. Observation — repeatable tests of answers, citations, brand descriptions, surfaced pages, and changes after publication.
- 6. Outcome — referral, visit, engaged session, lead, opportunity, and revenue when attribution is technically supportable.
This is not a vendor scorecard. It is an operating backlog. Each question should produce a concrete gap: a missing page, an ambiguous answer, unsupported proof, an inconsistent entity, blocked access, or incomplete measurement. If the diagnosis cannot change content, engineering, communications, or analytics work, it is not useful for investment decisions.
Fix access before rewriting the website
Confirm that priority pages can be crawled and indexed, essential content is available in HTML, canonical and hreflang point to the correct versions, internal links make the page discoverable, and the CDN, firewall, or consent layer does not hide content. Google recommends these fundamentals for its AI features. It also reports AI Overviews and AI Mode traffic inside Search Console's Web search type, not as a complete separate report. Search Console alone therefore cannot isolate every generative-discovery interaction.
For ChatGPT, OpenAI documentation separates OAI-SearchBot from GPTBot. A company may allow the search crawler while disallowing the crawler used for content that may support model training. The site owner should make that policy explicitly rather than treating training, search, and user-triggered visits as one control. Validate robots.txt, logs, and firewall rules against official user agents and IP ranges without opening access to unknown crawlers with similar names.
Turn internal knowledge into sources worth citing
A citable page answers early, shows how it reached the answer, and states context and limits. Replace unprovable leadership claims with useful evidence: service definitions, implementation workflow, buying criteria, clearly labeled hypothetical examples, methodology, responsibilities, cost drivers, and cases where the recommendation does not apply. Primary documentation should support external facts; editorial judgment should be labeled as such.
Structured data can give search systems explicit clues about page meaning when it matches visible content. It does not create authority or guarantee a citation. Use Organization, Article, Product, Service, or other relevant types only when they truthfully represent the page and validate correctly. An FAQ written only for markup does not repair a weak source.
Build a consistent company entity without duplicating every page
Define a short company description, services, served markets, official names, responsible people, and relationships between pages. Use that shared truth across the homepage, service pages, articles, legitimate external profiles, and structured data while adapting the language to context. Consistency is not duplication: a service page explains the offer and delivery; an article solves a question; an external source confirms facts about the organization.
For the U.S. market, publish evidence that fits local buying language, commercial structure, and applicable sources. Brazilian proof should not be translated into a U.S. claim without context. Spanish-speaking Latin America requires country-aware vocabulary, currency, regulation, and delivery assumptions. A regional hub can orient the reader, but high-consequence local questions deserve local sources.
An eight-week implementation scope
- Weeks 1–2: define 20–40 questions, preserve the baseline, select priority pages, map current sources, and name the commercial outcome.
- Weeks 3–4: audit crawl access, indexation, entity signals, architecture, content, structured data, analytics, and available attribution.
- Weeks 5–6: repair blockers and publish three to five high-intent assets with direct answers, evidence, internal links, and appropriate localization.
- Weeks 7–8: repeat tests, review logs and referrals, monitor visits and leads, document learning, and decide the next batch.
Eight weeks bounds the first cycle; it does not guarantee inclusion or recrawl timing. Deliverables should include the question inventory, preserved baseline, source and entity map, prioritized backlog, implemented fixes, published pages, editorial rules, dashboard, testing protocol, and named owners. Platforms change, and Google explicitly notes that crawl, indexation, and serving are never guaranteed.
Measure presence, quality, and value separately
- Presence — share of tested questions where the company or page appears, always tied to sample, date, and surface.
- Quality — correct description, appropriate source, right market, no invented claims, and meaningful coverage of the question.
- Traffic — identifiable referrals, landing pages, engagement, and repeat visits; some traffic may remain aggregated or lack a specific signal.
- Commercial — contacts, qualified leads, opportunities, and revenue where AI discovery appears in the journey, without causal claims the data cannot support.
Do not sell citations as a standalone KPI. A company can appear for irrelevant questions or be cited without a visit. A buyer can also start a conversation without a reliable referrer. Combine answer observation, analytics, self-reported attribution, and CRM records. When a platform aggregates data, state the limitation rather than filling it with an estimate.
Ownership, cost drivers, and trade-offs
Marketing owns questions, positioning, and the business outcome. Content turns knowledge into useful sources. Subject-matter experts validate claims. SEO and engineering own access and architecture. Analytics defines what can be measured. Communications and external partners build legitimate corroboration, not manufactured links. An agency or consultancy should connect these disciplines, ship site changes, and leave the team with an operable process.
Cost grows with languages, markets, service lines, CMS quality, source volume, specialist research, technical debt, log access, test frequency, and CRM integration. Broad content expands coverage but dilutes evidence; specific pages are more useful but require upkeep. Automation speeds monitoring, while unstable personalized answers still require human sampling. Start with high-intent questions and an architecture that can expand.
Bring real questions to the first conversation
Start with the first-workflow plan at https://makinai.co/insights/en/where-start-ai-marketing-90-day-first-workflow, review readiness at https://makinai.co/insights/en/assess-data-readiness-before-hiring-ai-company, and connect discovery to conversion at https://makinai.co/insights/en/ai-improve-website-conversion-without-replatforming. MAKINAI implements content architecture, SEO, GEO, and measurement: https://makinai.co/services/en/data-content-intelligence-systems. Bring ten buyer questions, five current pages, and any referral or lead evidence. We can identify the first discovery cycle worth building.
The sources support crawl controls, SEO fundamentals, reporting limitations, and the role of structured data. They do not demonstrate that this plan will make a company appear in a specific answer. The loop, scope, and metrics are MAKINAI editorial recommendations that require validation against the site, market, and monitored platforms.