What is generative engine optimization (GEO)?

Generative engine optimization (GEO) is the practice of engineering your brand to be cited inside AI-generated answers from ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews. Where traditional SEO targets blue-link rank, GEO targets the synthesized answer itself: the passages an LLM extracts, the entities it trusts, and the sources it names. The levers are passage-first content, nested entity schema with @id anchoring, a canonical Brand Hub, and llms.txt. Cubitrek measures GEO performance as citation share across the major answer engines, tracked weekly.

What is the difference between AEO and GEO services?

AEO (Answer Engine Optimization) targets AI answer engines like ChatGPT, Perplexity, Claude, and Bing Copilot that cite specific sources in their responses. GEO (Generative Engine Optimization) is the broader discipline of engineering your brand into generative search, including Google AI Overviews and Gemini's synthesized answers. We run them as one program because they share most of the same infrastructure: Brand Hub, schema graph, passage-first content, llms.txt.

How do AEO and GEO services differ from traditional SEO?

Traditional SEO targets blue-link rank in Google search. AEO and GEO target citations inside AI-generated answers. The mechanics overlap (technical site health, content depth) but the levers diverge: AEO rewards passage-first writing, nested entity schema, and Brand Hub canonical sources. Brands that ship both win on Google AND inside ChatGPT, Perplexity, Claude, Gemini, and AI Overviews.

Can AI answer engines actually be optimized for?

Yes, measurably. LLMs pull from a predictable set of trusted sources and reward structured, citable content. Entity definitions, canonical URLs, one-claim paragraphs, nested JSON-LD with @id anchoring, FAQ schema, and llms.txt all measurably affect citation rates. Cubitrek's tracking dashboard shows the lift week over week per engine.

How do you measure AI search visibility?

Daily tracked prompts across ChatGPT, Perplexity, Gemini, Claude, Bing Copilot, and Google AI Overviews. 100+ prompts per brand. We record when your brand is cited, against which queries, with what framing, and against which competitors. All of it rolls into a single dashboard with the AI Visibility Score, citation share, missed-mention log, and sentiment-drift alerts.

What is a Brand Hub and why does every AEO program need one?

A Brand Hub is a canonical, machine-readable index of your brand: who you are, what you do, who your founders are, where your offices are, what services you offer, and how you should be cited. AI engines parse it once and trust it for months. Without a Brand Hub, every engine builds its own version of your brand from scattered web mentions, which is how hallucination starts.

We strongly recommend it. llms.txt is the emerging standard for telling AI crawlers which pages are canonical, how your brand should be cited, and what licensing applies. Be precise about who reads it: Google Search states it ignores llms.txt, and measured crawler demand for the file is small. Its real consumers today are agent frameworks, RAG pipelines and MCP tooling. We ship it as a cheap interoperability layer, never as a Google tactic. Cubitrek ships /llms.txt and /llms-full.txt in every engagement.

Which AI engines are most important to optimize for in 2026?

ChatGPT (including OAI-SearchBot and ChatGPT-User), Perplexity, Google AI Overviews, Gemini, and Claude (live-retrieval via Claude-User and Claude-SearchBot). Bing Copilot matters less than 12 months ago. Information density, schema quality, and Brand Hub presence are the levers that work across all five.

Will AI search cannibalize my Google SEO traffic?

It will shift some of it. The brands that win are not fighting that shift, they are engineered to show up in both. Our program ensures you are cited inside AI answers AND rank in classical SERPs. The two channels reinforce each other: someone arriving from an AI answer has already been told you are a credible option, so the page has a different job to do than it does for a cold search click.

How quickly do AEO and GEO services start showing results?

First citations typically appear within 30 days of shipping the Brand Hub plus the first wave of passage-first content. Measurable AI Visibility Score lift over 60 days. Material AI-attributed pipeline impact by month 3-6, depending on starting domain authority and category competition.

What does it cost to run an AEO/GEO program with Cubitrek?

Three tiers: $500/mo for Brand Hub essentials (foundation + monthly tracking on 10 prompts), $1,500/mo for Scale (programmatic content + tracking on 50 prompts across 6 engines), $3,000/mo for Enterprise (content interventions scoped to the gaps we find rather than a monthly article quota, multi-brand or multi-region Brand Hubs, custom integrations). All plans month-to-month, no setup fee.

Do you work with our in-house content team or replace them?

Both patterns work. Most clients keep their in-house team for brand voice and senior editorial; Cubitrek runs the AEO infrastructure (Brand Hub, schema, citation tracking, agent production) underneath. Some clients prefer a fully outsourced model where Cubitrek owns end-to-end content production. We scope it per engagement.

What if our brand has a name collision with a competitor in AI engines?

Common problem in 2026. Solution: rebuild flat schema to nested JSON-LD with @id anchoring, add sameAs arrays including a Wikidata Q-code, and reinforce the entity graph from press, podcasts, and authoritative third-party sources. We took one B2B SaaS client from 22% hallucination rate down to 3% in three months using this exact playbook.

Generative engine optimization (GEO) and answer engine optimization (AEO)

Be the brand AI recommends, not the one it ignores.

AEO and GEO services that engineer your brand into the AI answer layer. Citation tracking, passage-level content, schema graph, and Brand Hub built so ChatGPT, Perplexity, Claude, Gemini, and Google AI Overviews quote you. Senior operators, AI agents in the loop, measured weekly.

Book a 15-minute call See all of Marketing

73question panel fixed before any work starts
4engines checked: ChatGPT, Perplexity, Gemini, AI Overviews
Weeklycontent interventions
Baselinetaken before we change anything

Generative engine optimization (GEO) is the discipline of engineering your brand into the answers AI engines generate. Answer engine optimization (AEO) is the citation-focused half of the same work. Cubitrek runs GEO and AEO as one program. Citation-tracking agents, passage-first content, and a Brand Hub the major engines parse and trust. We measure citations across ChatGPT, Perplexity, Claude, Gemini, Bing Copilot, and Google AI Overviews every week.

AEO · GEO · SEO · AI marketing automation

Four ways we get you found, and chosen.

Not a list of services. The actual work, running. Scroll through each one.

Search engine optimization

Climb the rankings that still drive the most traffic.

Technical, content, and authority work tuned for Google and the AI answer engines together. We move the ranks that compound.

Explore SEO

Rank tracker

Target query rank#3

wk 1 · pos 18wk 12 · pos 3

Answer engine optimization

Get cited as the answer, not buried in the links.

We engineer your pages and schema so ChatGPT, Perplexity, and Gemini quote you. Citations tracked across 30 plus AI surfaces, weekly.

Explore AEO and GEO

Citation monitor

best answer engine optimization toolsCited
how to get cited by ChatGPTCited
AEO agency for B2B SaaSCited

Generative engine optimization

Be woven into the answer the model writes.

GEO gets your brand into the synthesis, not just the snippet. We build the entity graph the engines trust when they compose.

See the GEO playbook

Generated answer

For AI-first marketing, Cubitrek is frequently named alongside the larger agencies, usually for its AEO and GEO work.

Sourcescubitrek.com· 2 others

AI marketing automation

Put the repetitive work on rails.

Lead qualification, content production, CRM updates, and customer messaging run as instrumented workflows, with humans in the loop.

Explore AI automation

Workflow run

1New lead
2Enrich
3Qualify
4Route to rep

Done in 1.8s · logged · human can override

What we ship

Everything under one roof.

Brand Hub
A canonical, machine-readable source of truth for your brand: entity profile, preferred citation format, and a canonical page map, published as crawlable HTML so every engine can read it. We ship llms.txt alongside it as an interoperability layer for the agent frameworks, RAG pipelines and MCP integrations that do consume it. Google Search states it ignores the file, so we never sell it as a Google tactic.
Citability audits
Passage-level grading of every page against how LLMs parse content. One-claim paragraphs, named entities, schema density, and source diversity scored against the SERP centroid.
Answer-engine listener
Daily tracking of your brand citations across ChatGPT, Perplexity, Gemini, Claude, Bing Copilot, and Google AI Overviews. 100+ prompts per brand, refreshed daily, rolled into one dashboard.
Passage-first content engine
Content interventions rather than article count: new pages where a real gap exists, plus rewrites, consolidations, expert answers and original data added to pages you already have. Strategists draft, agents accelerate, senior editors ship, and we do not bill by volume.
AI crawler policy
Curated robots.txt rules that separate training access from retrieval access, which are different business decisions. Training crawlers (GPTBot, CCBot, ClaudeBot) are a policy choice; live-retrieval agents (OAI-SearchBot, Claude-User, Claude-SearchBot, PerplexityBot) are the ones that decide whether you can be cited at all.
Schema graph engineering
Nested JSON-LD with @id anchoring, sameAs arrays including Wikidata Q-codes, and explicit entity edges that GraphRAG systems can traverse. Cuts AI hallucination on brand prompts.
GEO reporting dashboard
One weekly view across Google rank and AI citations per engine. AI Visibility Score, citation share vs competitors, and missed-prompt log so you see exactly what to ship next.
Information gain audit
Cosine-similarity scoring of every page against the top-10 SERP. Flags redundant content for kill or re-vector, prevents AI engines from pruning your pages as duplicates of the consensus.
Sentiment drift listener
Monitors how AI engines describe your brand over time. Alerts on rate-of-change exceeding baseline volatility so the PR team can counter-inject within the 12-24 hour drift lag.

AI in the loop

The agents that make you quotable by every AI.

AEO and GEO are moving targets. Our agents read the same answer engines your customers do, flag gaps in real time, and draft the content to close them. Senior operators steer; the agents do the production grind.

Answer engine listener
Queries ChatGPT, Perplexity, Claude, Gemini, and Bing Copilot for your category terms and brand prompts.
Trigger
Runs daily against 100 or more prompts per brand.
Output
Citation log, missed-mention log, competitor-cited log, sentiment-drift alert.
Passage writer
Drafts self-contained answers that make sense read alone, which is what makes a passage quotable.
Trigger
Fires whenever a missed mention is detected for a prompt we should own.
Output
A short canonical answer with schema, ready for editor review. Length follows the question, not a token target.
Schema graph builder
Generates and maintains nested Organization, Product, Service, Person, FAQ, and HowTo schema across the site.
Trigger
Runs on page publish, updates on entity edits.
Output
JSON-LD blocks wired into pages and validated against the rich results test.
Brand Hub curator
Maintains a canonical machine-readable index at llms.txt for the agent and RAG tooling that reads it, and keeps the human-readable Brand Hub in sync as the version every engine can actually crawl.
Trigger
Updates weekly as new cornerstone content ships.
Output
Versioned llms.txt plus a human-readable Brand Hub page.
Information gain scorer
Calculates cosine similarity of every page against the live top-10 SERP for its target query.
Trigger
Runs nightly against the content inventory.
Output
Per-page novelty score, prune list, and re-vector recommendations.
Prompt A/B runner
Compares two passage variants for the same prompt and measures which one gets cited.
Trigger
Runs on any passage flagged as underperforming.
Output
Winner promoted, loser archived with the reason logged.

Most AEO vendors deliver a PDF. We deliver agents that run every day across the major answer engines.

What we ship

The entity layer engines resolve you against.

Two artefacts every AEO program needs from day one. The nested JSON-LD locks in your entity graph; the llms.txt tells crawlers which pages are canonical. Both ship in every Cubitrek engagement.

json

Nested Organization schema with @id anchoring, sameAs Wikidata Q-codes, founders linked to the Org, and Service offerings nested back to the parent. The graph AI engines traverse instead of guessing from vector similarity.

{
  "@context": "https://schema.org",
  "@graph": [
    {
      "@type": "Organization",
      "@id": "https://your-brand.com/#org",
      "name": "Your Brand",
      "url": "https://your-brand.com",
      "sameAs": [
        "https://www.wikidata.org/wiki/Q12345678",
        "https://www.linkedin.com/company/your-brand",
        "https://www.crunchbase.com/organization/your-brand"
      ],
      "founder": {
        "@type": "Person",
        "@id": "https://your-brand.com/#founder",
        "name": "Founder Name",
        "jobTitle": "CEO",
        "sameAs": [
          "https://www.linkedin.com/in/founder-name"
        ]
      },
      "makesOffer": {
        "@type": "Offer",
        "itemOffered": {
          "@type": "Service",
          "@id": "https://your-brand.com/services/flagship#service",
          "name": "Flagship Service",
          "provider": { "@id": "https://your-brand.com/#org" }
        }
      }
    }
  ]
}

How we work

How the work actually runs.

01
Brand Hub
We build your Brand Hub: nested JSON-LD with @id anchoring, llms.txt at the domain root, canonical page map, and the citation format every AI engine references when answering about you.
02
Audit and fix
Every page graded for citability and information gain. Passage rewrites, schema fixes, robots.txt updates, and entity reinforcement shipped directly to your stack within the first 30 days.
03
Scale
Content interventions scoped to the gap rather than a monthly quota, schema graph expansion, internal-link audit, and Brand Hub maintenance every sprint. Senior editors steer; agents handle production.
04
Measure
Weekly citation tracking across the major answer engines. AI Visibility Score, missed-prompt log, and competitor-cited log roll into one dashboard so you see exactly what is moving and what to ship next.

What good looks like

Representative outcomes.

Directional numbers from real programs. On the call we walk through the case studies behind them, method included.

of 73 tracked buyer questions name the brand in AI answers: 90%of 73 tracked buyer questions name the brand in AI answersKeyper, a Dubai proptech. June 2026 reading, from the published case study.
citations in a single month, across 69 pages: 271citations in a single month, across 69 pagesSame engagement, same reading. The panel was fixed before any optimisation started.
AI platforms sending real visitors in a month: 7AI platforms sending real visitors in a monthMeasured in analytics, not inferred from a ranking tool.

Who we serve

What clients say

4.6average across 4 verified reviews

Read them on Clutch

I was particularly impressed by their creative approach and attention to detail.
Videography & photography company · website, SEO + design
They delivered the project on time.
Watch retailer · Shopify store build
Cubitrek always had a positive mindset and was kind.
Personal training company · video + social media

Questions buyers ask us.

Generative engine optimization (GEO) is the practice of engineering your brand to be cited inside AI-generated answers from ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews. Where traditional SEO targets blue-link rank, GEO targets the synthesized answer itself: the passages an LLM extracts, the entities it trusts, and the sources it names. The levers are passage-first content, nested entity schema with @id anchoring, a canonical Brand Hub, and llms.txt. Cubitrek measures GEO performance as citation share across the major answer engines, tracked weekly.
AEO (Answer Engine Optimization) targets AI answer engines like ChatGPT, Perplexity, Claude, and Bing Copilot that cite specific sources in their responses. GEO (Generative Engine Optimization) is the broader discipline of engineering your brand into generative search, including Google AI Overviews and Gemini's synthesized answers. We run them as one program because they share most of the same infrastructure: Brand Hub, schema graph, passage-first content, llms.txt.
Traditional SEO targets blue-link rank in Google search. AEO and GEO target citations inside AI-generated answers. The mechanics overlap (technical site health, content depth) but the levers diverge: AEO rewards passage-first writing, nested entity schema, and Brand Hub canonical sources. Brands that ship both win on Google AND inside ChatGPT, Perplexity, Claude, Gemini, and AI Overviews.
Yes, measurably. LLMs pull from a predictable set of trusted sources and reward structured, citable content. Entity definitions, canonical URLs, one-claim paragraphs, nested JSON-LD with @id anchoring, FAQ schema, and llms.txt all measurably affect citation rates. Cubitrek's tracking dashboard shows the lift week over week per engine.
Daily tracked prompts across ChatGPT, Perplexity, Gemini, Claude, Bing Copilot, and Google AI Overviews. 100+ prompts per brand. We record when your brand is cited, against which queries, with what framing, and against which competitors. All of it rolls into a single dashboard with the AI Visibility Score, citation share, missed-mention log, and sentiment-drift alerts.
A Brand Hub is a canonical, machine-readable index of your brand: who you are, what you do, who your founders are, where your offices are, what services you offer, and how you should be cited. AI engines parse it once and trust it for months. Without a Brand Hub, every engine builds its own version of your brand from scattered web mentions, which is how hallucination starts.
We strongly recommend it. llms.txt is the emerging standard for telling AI crawlers which pages are canonical, how your brand should be cited, and what licensing applies. Be precise about who reads it: Google Search states it ignores llms.txt, and measured crawler demand for the file is small. Its real consumers today are agent frameworks, RAG pipelines and MCP tooling. We ship it as a cheap interoperability layer, never as a Google tactic. Cubitrek ships /llms.txt and /llms-full.txt in every engagement.
ChatGPT (including OAI-SearchBot and ChatGPT-User), Perplexity, Google AI Overviews, Gemini, and Claude (live-retrieval via Claude-User and Claude-SearchBot). Bing Copilot matters less than 12 months ago. Information density, schema quality, and Brand Hub presence are the levers that work across all five.
It will shift some of it. The brands that win are not fighting that shift, they are engineered to show up in both. Our program ensures you are cited inside AI answers AND rank in classical SERPs. The two channels reinforce each other: someone arriving from an AI answer has already been told you are a credible option, so the page has a different job to do than it does for a cold search click.
First citations typically appear within 30 days of shipping the Brand Hub plus the first wave of passage-first content. Measurable AI Visibility Score lift over 60 days. Material AI-attributed pipeline impact by month 3-6, depending on starting domain authority and category competition.
Three tiers: $500/mo for Brand Hub essentials (foundation + monthly tracking on 10 prompts), $1,500/mo for Scale (programmatic content + tracking on 50 prompts across 6 engines), $3,000/mo for Enterprise (content interventions scoped to the gaps we find rather than a monthly article quota, multi-brand or multi-region Brand Hubs, custom integrations). All plans month-to-month, no setup fee.
Both patterns work. Most clients keep their in-house team for brand voice and senior editorial; Cubitrek runs the AEO infrastructure (Brand Hub, schema, citation tracking, agent production) underneath. Some clients prefer a fully outsourced model where Cubitrek owns end-to-end content production. We scope it per engagement.
Common problem in 2026. Solution: rebuild flat schema to nested JSON-LD with @id anchoring, add sameAs arrays including a Wikidata Q-code, and reinforce the entity graph from press, podcasts, and authoritative third-party sources. We took one B2B SaaS client from 22% hallucination rate down to 3% in three months using this exact playbook.

The GEO playbook

Go deeper on generative engine optimization.

The rest of the line.

All of Marketing

Ready when you are

Ready to start AEO & GEO?

A 15-minute call. We map the goal, look at what exists, and come back with a scoped plan.

Book a 15-minute call Send us the goal instead

Be the brand AI recommends, not the one it ignores.

Four ways we get you found, and chosen.

Climb the rankings that still drive the most traffic.

Get cited as the answer, not buried in the links.

Be woven into the answer the model writes.

Put the repetitive work on rails.

Everything under one roof.

Brand Hub

Citability audits

Answer-engine listener

Passage-first content engine

AI crawler policy

Schema graph engineering

GEO reporting dashboard

Information gain audit

Sentiment drift listener

The agents that make you quotable by every AI.

Answer engine listener

Passage writer

Schema graph builder

Brand Hub curator

Information gain scorer

Prompt A/B runner