What is GEO and how is it different from SEO?

GEO targets how AI systems cite and surface your content, not how Google ranks it. Traditional SEO optimizes for crawlers and backlink signals. GEO optimizes for how large language models extract answers: content structure, entity clarity, schema specificity, and whether third-party sources corroborate your claims. The techniques overlap in places but the execution is fundamentally different.

Which AI platforms does GEOhelp optimise for?

GEOhelp optimizes for the three major AI search platforms: ChatGPT (including Browse and SearchGPT), Perplexity AI, and Claude. We run test queries across all three to establish a baseline and measure improvement, since each platform has slightly different extraction and citation behaviors.

How long does it take to see results in AI search?

Structural and schema changes can start showing within 2–6 weeks. Citation frequency usually improves over a 2–3 month cycle. The timeline depends on the client's existing domain authority, how competitive the query space is, and whether the gaps are structural (fast to fix) or authority-based (slower, requires third-party citations).

Do my clients need to know GEOhelp is involved?

No. GEOhelp is fully white-label, so your client relationship stays yours.

What does a monthly GEO execution retainer include?

One client slot. Each month: we audit 15+ prompts across all three AI platforms, identify the top three fixes ranked by expected impact, implement those fixes (content rewrites, schema changes, citation outreach), retest the same query set, and deliver a summary your agency can send to the client as-is.

How do I know if my clients need GEO work?

The clearest signal: a competitor appears consistently in AI answers for industry queries and your client doesn't. If you're already tracking this in Otterly or Peec.ai and seeing low scores, GEO work is the execution layer that moves those numbers.

Guide

How to Rank in AI Search

Improving AI search visibility is a repeatable five-step process. It is not a one-time fix — AI platforms update their extraction behaviour, new competitors enter the space, and citation authority builds incrementally. But the core cycle is consistent: audit, analyse, fix, retest.

Audit your current AI visibility

Before changing anything, establish a baseline. Select 15–25 prompts that a real buyer would type — questions your client's customers actually ask. Run those prompts across ChatGPT, Perplexity, Google AI Overviews, and Microsoft Copilot. Record whether the brand appears, in what position, and with what language.

Tools like Otterly.AI and Peec.ai can automate this tracking. For a thorough baseline, combine automated monitoring with manual testing — AI platforms sometimes serve different results depending on user context and timing.

Do not skip this step. Without a baseline, you cannot measure improvement, and improvement is the only thing the client cares about.

Identify the gap type

Not all AI visibility failures have the same cause. The audit tells you the gap exists; gap analysis tells you why. There are three types of gap:

Structural gaps

Content exists but is not written in a format LLMs can extract. Long paragraphs, vague definitions, and no direct answers to the prompts being tested. Fix: rewrite.

Technical gaps

Content exists but lacks the schema types that signal what the page is about to AI crawlers. Fix: deploy FAQPage, Article, HowTo, Organization schema.

Authority gaps

Content is well-structured and schema is in place, but no third-party sources corroborate the brand's claims. LLMs won't cite sources they can't verify. Fix: citation building on independent platforms.

Restructure content for LLM extraction

AI systems extract answers by scanning for the clearest, most direct response to a query. Content written for engagement metrics — long introductions, keyword stuffing, narrative structure — is harder to extract from than content written for extractability.

›Lead every section with the direct answer, then expand
›Use explicit question-and-answer pairs where possible
›Define your entity clearly on every key page: who you are, what you do, where you operate, who you serve
›Use short paragraphs (2–4 sentences) rather than dense blocks
›Avoid ambiguity: if you do X for Y clients at Z price range, say so explicitly

Deploy the correct schema types

Schema markup tells AI crawlers what your content means, not just what it says. The schema types most valuable for GEO are different from the ones SEO practitioners typically prioritise:

FAQPageAny page with question-and-answer content. Makes individual Q&As extractable as standalone answers.

ArticleGuides, explainers, and thought leadership content. Signals author, publisher, and publication date.

HowToStep-by-step process pages. Each step becomes independently extractable.

OrganizationYour homepage and about page. Defines the entity: name, description, founder, services, area served.

ServiceIndividual service pages. Describes what you offer, for whom, and at what price range.

Build authoritative citations

Language models learn from text on the public web. When multiple independent sources describe your brand, product, or service in consistent terms, that entity becomes more citable. Citation building in GEO is different from link building in SEO:

›Focus on mentions, not just links — AI models parse text, not link graph metrics
›Target platforms that AI models crawl: industry directories, editorial sites, press mentions, Q&A platforms like Quora and Reddit
›Ensure consistency: your brand name, description, and service area should be described identically across sources
›Prioritise sources that AI models already cite frequently in your category
›Guest posts and contributed articles on relevant publications create both a citation and a context signal

Citation building takes the most time. Structural and schema changes can show results in weeks; citation authority builds over 2–3 months. Plan accordingly.

Retest and repeat

After implementing fixes, run the same prompt set again. Compare citation rates, brand mention frequency, and the quality of the mentions (cited as an authority vs. mentioned in passing). Use this data to prioritise the next round of fixes.

GEO is an iterative process. The brands that appear most consistently in AI answers are not the ones who did GEO once — they are the ones who treat it as an ongoing discipline.

See what this looks like in practice: GEO Examples →