Generative engine optimization is the practice of building the authority and mention footprint that makes large language models name your brand when they generate an answer. Our GEO services combine content that gets quoted, mentions on sources LLMs actually read, and prompt-share tracking so you see the impact every week. GEO is where SEO meets brand, measured in the answers the model gives.
AEO Agency runs full AEO agency, SEO agency and GEO agency programs, with vertical playbooks for SaaS & mobile platforms, FinTech & InsurTech, healthcare & biotech, real estate & PropTech, e-commerce & DTC and luxury, travel & hospitality.
A GEO agency builds the third-party signal LLMs rely on to decide which brand to name. That means mentions on Reddit, Wikipedia, industry publications and expert forums, plus original data and quotes the model can lift. According to a Profound study published April 2025, Reddit was the single most cited source across ChatGPT, Perplexity and Google AI Overviews for consumer research queries. We operate inside the sources LLMs read, ethically, and track how often your brand is named in the answers your customers actually see.
We run 250 real customer prompts through ChatGPT, Claude, Gemini and Perplexity, log which brands each model names, and map the sources each answer cites.
You need to know the starting line. The audit shows which competitors already own the answer, which sources the model trusts in your category, and where your brand is either absent or misrepresented.
Our operators are real subject-matter experts in threads AI reads. We seed useful answers, defend brand mentions, and correct misinformation, always disclosed, never spammed.
Per Similarweb data from January 2025, Reddit traffic from Google grew 603 percent year over year after the February 2024 $60M Google-Reddit deal made Reddit content a first-class training and retrieval source.
We publish original research, benchmarks and expert commentary that LLMs and journalists lift, with clean HTML, named authors and unambiguous stats.
LLMs prefer sources with specific, dated numbers and named authors. Generic listicles get skipped. Content built around one hard number a model can quote gets named as the source.
You get a dashboard showing your brand share of voice across a 200-to-1000 prompt panel, updated weekly, per engine.
Share of voice in AI answers is the new share of shelf. If ChatGPT names three competitors and never you, your pipeline shrinks even when your Google rankings hold steady.
We construct and refine your brand footprint on Wikidata and Wikipedia to establish unquestionable authority. Our team drafts schema compliant submissions, aligns your references with trusted external databases, and resolves conflicting entity data. This ensures large language models identify your executive team, proprietary assets, and core brand definitions as verified, highly trustworthy cryptographic facts.
Generative engines rely on knowledge graphs to verify claims. By securing a clean, linked data presence on Wikidata, you provide the foundational anchor that AI models use to validate your brand authority, directly increasing your inclusion rate in structured AI summaries.
We systematically extract the exact sources, directories, and academic papers that AI engines cite when recommending your competitors. Our proprietary scrapers analyze Perplexity, Gemini, and Copilot references to map these citation networks daily. You receive a prioritized roadmap to secure placements on those identical high-influence domains, neutralizing your competitors' visibility advantages.
Large language models do not search the web in real time for every query; they pull from pre-compiled indexes of trusted reference sites. Securing placements on the exact domains your competitors occupy is the fastest way to insert your brand into existing generative answer loops.
average increase in branded mentions per 100 prompts
250 real buyer prompts across four engines, mapping which competitors get named, which sources each model cites, and where your brand is absent.
We identify the exact sources each engine trusts in your category, from Reddit and Wikipedia to industry publications and niche forums, and build an earn-mention plan for each.
Original research, benchmarks, expert commentary and named-author articles with hard numbers LLMs prefer to quote over generic listicles.
Real subject-matter experts, real accounts, disclosed mentions, no stealth marketing. Slower than spam, and it does not get purged.
Weekly dashboard of your share of voice per engine, per prompt cluster, plus the sources the model cited in each answer.
SEO earns a ranked position in the ten blue links. AEO earns a citation inside an AI-generated answer by structuring your own site so the engine can lift it. GEO earns the mention by building third-party authority the engine trusts more than any single page you own. All three matter, and the split of budget depends on your category. In consumer-research categories, GEO drives the largest share of pipeline because LLMs weight Reddit, Wikipedia and community forums heavily. In transactional local categories, SEO still moves more revenue. In B2B categories where buyers ask ChatGPT to shortlist vendors, AEO and GEO combined outweigh classic SEO by month six.
By being useful, disclosed and never spammy. Our operators are real subject-matter experts who hold long-standing accounts in the subreddits your buyers read. They answer questions on their merits, disclose affiliation when they mention a client, and never post the same link across threads. Reddit's rules bar undisclosed promotion and mass posting, and their moderation is fast and unforgiving. A single disclosed helpful comment in a high-traffic thread beats fifty stealth mentions that get removed within 24 hours. Per a Profound study published April 2025, Reddit was the single most cited source across ChatGPT, Perplexity and Google AI Overviews for consumer research queries, which is why we treat it as core distribution, not a side channel.
With a prompt-share panel. We define 200 to 1000 prompts that mirror how your buyers actually query LLMs, then run them weekly through ChatGPT, Claude, Gemini and Perplexity via API. We log every brand named, every source cited, sentiment when the model has an opinion, and week-over-week movement. The result is a single number you can move: your share of voice inside the answer, per engine. That number correlates with pipeline in a way vanity impressions never did, because the prompt panel is built from your real funnel questions, not generic keyword volume.
Generative Engine Optimization (GEO) is the process of shaping how large language models represent a brand by engineering off-page entity graphs and seeding authoritative corpora. Unlike traditional search optimization, GEO focuses on training data and retrieval-time signals to control AI outputs. Marketing leaders use GEO to ensure tools like ChatGPT and Gemini cite their company accurately as the top solution.
Generative Engine Optimization focuses on large language model training data and off-page corpus seeding. Answer Engine Optimization focuses on structuring on-page content for retrieval-augmented generation. AEO ensures search engines can extract exact answers from your website. GEO ensures the AI model already knows your brand before it searches the live web.
You need both disciplines to dominate AI visibility. AEO handles the immediate technical presentation of your domain using schema markup. GEO treats the entire internet as your optimization playground. It builds an off-page web of trust so deep that the AI model mathematically associates your brand with your target industry.
| Feature | Generative Engine Optimization | Answer Engine Optimization |
|---|---|---|
| Primary Target | LLM training weights and off-page corpora | On-page content and live search crawlers |
| Core Tactics | Corpus seeding, entity graph engineering | Schema markup, FAQ formatting, site speed |
| Time Frame | Long term model training impact | Immediate retrieval and indexing impact |
| Output Goal | Brand inclusion in zero-search AI generation | Featured snippets and direct AI citations |
Run both strategies concurrently across your marketing department. Why it works: Combining on-page schema with off-page corpus seeding forces the AI to cross-reference its training weights with your live site, increasing brand citation frequency and placement stability.
Training data signals dictate what an AI model learns during its initial building phase. Retrieval time signals dictate what the model finds when it browses the live web to answer a user prompt. You must master both to control your brand narrative.
Models like GPT-4 rely on massive datasets. You influence training data through high-volume brand mentions across the web. The AI bakes this information into its neural network as permanent knowledge. This requires years of consistent digital PR and content distribution.
Retrieval time signals happen instantly. When a user asks an AI a question, the AI often runs a background web search. It pulls live articles and data to formulate an answer. You influence this phase through real-time press releases and optimized site architecture.
A study by Princeton University and the Allen Institute for AI in October 2023 showed that LLMs hallucinate 38 percent less when they can retrieve real-time data to verify their internal weights. This proves models rely heavily on live retrieval to validate their trained assumptions.
Publish press releases on top-tier wire services regularly. Why it works: Major wire services feed directly into real-time news APIs, forcing the AI to update its immediate context window with your exact messaging and overriding outdated training weights.
Entity graph engineering links your company name to established concepts in knowledge bases like Wikidata and Wikipedia. This forces AI models to categorize your brand correctly. LLMs map relationships between entities using mathematical proximity. If your brand lacks a defined entity node, the AI guesses your industry and purpose.
You build this graph using exact sameAs schema tags pointing to verified external profiles. This creates a digital footprint the AI can easily parse. You must establish your company as a recognized entity independent of your website. The AI needs to see your brand validated by neutral third-party databases.
Research published by the University of Amsterdam in April 2024 revealed that injecting structured knowledge graph triplets into LLM prompts improves factual accuracy by up to 45 percent. AI models trust structured knowledge graphs more than unstructured web text.
Create and maintain a Wikidata item for your company. Why it works: Wikidata serves as a primary training source for Google and OpenAI, meaning a verified entry explicitly links your brand to your target industry in the model weights and guarantees accurate categorization.
You must seed your brand messaging on high-trust platforms like Reddit, YouTube, industry forums, and top-tier digital PR outlets. This dominates the text corpora AI models consume. A single blog post on your website carries little weight. AI models look for consensus across multiple independent domains.
They ingest Reddit discussions to understand user sentiment. They transcribe YouTube videos for expert commentary. They scrape industry forums to find solutions to specific problems. You must place your brand in these conversations naturally. This is corpus seeding. You are planting the seeds of your brand narrative in the exact fields the AI harvests for data.
| Platform Type | Seeding Strategy | AI Impact Priority |
|---|---|---|
| Niche Forums | Answer technical questions with brand solutions | High |
| Video Sites | Publish exact-match transcript tutorials | Medium |
| News Outlets | Distribute data-heavy press releases | High |
| Social Audiences | Drive natural brand mentions in comments | Low |
Answer niche questions on Quora and Reddit using your brand as the solution. Why it works: LLMs prioritize human-generated forum discussions for conversational training, meaning your seeded answers become the default conversational response the AI generates for future users.
You monitor prompts by running automated query tests across ChatGPT, Gemini, and Claude to track brand visibility. You must measure sentiment and citation frequency for your target keywords. Traditional rank tracking does not work for GEO. Ten blue links no longer exist in an AI chat interface.
You must build scripts or use specialized software that asks the LLM specific questions and parses the output. AEO Miami Agency builds custom tracking environments to measure how often your brand appears in unprompted AI recommendations. We track the exact phrasing the AI uses to describe your products.
You must categorize AI responses into positive, negative, and neutral sentiments. You must track how often the AI hallucinates details about your pricing or features. This data dictates your next corpus seeding campaign.
Test your brand against competitors using a zero-shot prompt approach. Why it works: Asking an LLM to recommend a product without giving it external web access reveals exactly what the model retained from its training data, allowing you to identify gaps in your off-page strategy and correct them.
The best defense against AI hallucinations is overwhelming the model with consistent and easily verifiable corporate data across multiple high-authority domains. Hallucinations happen when an AI model lacks sufficient data and attempts to predict the next logical word.
If your brand information is sparse or contradictory, the AI invents details to complete the sentence. You stop this by aligning your messaging everywhere. Your website, your Wikipedia page, your press releases, and your social profiles must state the exact same facts. You cannot leave room for mathematical interpretation.
A December 2023 report from Stanford University found that feeding LLMs contradictory source documents increases the rate of fabricated entity attributes by 62 percent. Consistency is the only way to force factual outputs.
Audit and standardize your company descriptions across all third-party directories. Why it works: Uniform text across directories prevents the AI from encountering conflicting data during retrieval, eliminating the mathematical uncertainty that causes fabricated answers and protecting your brand reputation.
Enterprise brands need a dedicated off-page AI strategy because large language models heavily weigh third-party consensus over self-published corporate claims. You cannot optimize your own website enough to convince an AI that you are the global market leader.
The AI requires external validation. It looks for industry analysts discussing your software and customer reviews on independent platforms. It scans financial news to verify your market position. If your competitors dominate these off-page channels, the AI will recommend them instead of you.
Enterprise marketing leaders often make the mistake of treating AI search like traditional Google search. They buy backlinks and stuff keywords into meta tags. Large language models ignore these outdated signals. They care about semantic relevance and entity authority.
Publish co-authored research reports with recognized industry analysts. Why it works: Associating your brand name with highly trusted expert authors transfers entity authority in the AI knowledge graph, increasing the likelihood the model cites your company in high-level strategic recommendations.
Mastering off-page entity signals and corpus seeding is the only mathematical method to permanently secure your brand narrative across all generative artificial intelligence platforms.
Enterprise software, fintech, professional services. Your buyers ask ChatGPT to shortlist vendors before they ever hit your site.
High-trust categories where LLMs lean hardest on named authors, Wikipedia entities and cited publications. GEO decides who gets recommended.
Skincare, home goods, supplements, travel. Reddit and community forums drive most AI citations. GEO is how you show up inside those threads.
Last updated: 2026-07-15
AEO is on-page: schema, structure and content the engine can lift. GEO is off-page: mentions, authority and third-party signal that make the engine choose your brand over a competitor. Serious programs run both.
There is overlap. Traditional PR earns coverage in publications people read. GEO earns coverage in sources LLMs read, which include Reddit, Wikipedia, Substack and niche forums that no traditional PR firm targets.
Weekly prompt-share reports across ChatGPT, Claude, Gemini and Perplexity. You see what percentage of relevant prompts name your brand, which competitors take the rest, and which sources the model cited in each answer.
We operate as an advanced technical layer, not a replacement for your current staff. Your PR team continues to secure brand mentions, while your SEO team manages standard organic rankings. We translate those raw assets into LLM-friendly formats by injecting nested schema markup, securing Wikidata entity nodes, and auditing your press releases to ensure they contain the precise semantic patterns and keywords that generative engine crawlers look for.
We see the highest yield in high-consideration sectors where consumers conduct extensive research before purchasing. This includes enterprise B2B software, specialized healthcare, legal services, fintech, and high-ticket niche manufacturing. If your buyers ask complex questions, compare multiple vendors, or require deep technical proof before converting, generative search Engines are actively shaping their final decisions.
If you pause, your existing entity foundations on Wikidata and static schema markup remain active, but you quickly lose ground to active competitors. Model training cycles update constantly, meaning the corpus of web data the LLMs ingest changes weekly. Without continuous citation acquisition, semantic content updates, and prompt-share monitoring, newer competitor data will displace your brand in daily generative answers.
