Generative Engine Optimization

GEO: Be the source LLMs retrieve

When a generative AI assembles a response about your industry, it draws from a pool of sources it has indexed and determined trustworthy. GEO is the practice of ensuring your content is in that pool. Different from AEO in focus: less about being the single cited answer, more about being part of what gets built into the response.

Get a GEO Assessment

Compare AEO vs GEO

65%

Of LLM responses draw from 5+ sources

More retrieval for semantically dense content

8wk

Typical time to first GEO retrieval gains

0.71

Correlation: web mentions to AI visibility (Ahrefs)

What GEO Covers

Five disciplines that drive LLM retrieval.

LLMs retrieve content that is deep, structured, authoritative, and topically dense. GEO builds all four qualities into your content architecture.

Semantic Content Density

Shallow content does not get retrieved. GEO requires pages that cover a topic with the kind of depth an LLM needs to generate a useful response. We build content clusters with the semantic density that makes your domain authoritative to AI retrieval systems.

Topic cluster architecture design
Content depth audits against retrieved competitors
Semantic keyword clustering per cluster
Supporting page development at scale

Structured Data for Synthesis

LLMs parse structured data more reliably than unstructured prose. We implement the schema markup patterns that make your content machine-readable: Article, HowTo, FAQ, Dataset, and structured product and service markup that AI systems can extract and attribute.

Article and HowTo schema implementation
Dataset and Table markup for data-heavy pages
Product and Service schema optimization
BreadcrumbList and SiteLinksSearchBox

Topical Authority Architecture

AI systems evaluate sources at the domain level. A site that covers one topic deeply is more likely to be retrieved than a site that covers many topics shallowly. We design topical authority structures that signal domain expertise to retrieval pipelines.

Topical silo architecture design
Internal linking for authority consolidation
Pillar and cluster content planning
Competing page consolidation and redirects

Original Data and Research

LLMs preferentially retrieve primary sources. Original survey data, original research, proprietary statistics. If your content cites industry reports, you are competing with the reports themselves. If you publish original data, you become the primary source.

Original research design and publication
Data visualization for shareability
Statistics page architecture
Research syndication for co-citation building

GEO Performance Tracking

We track retrieval through structured prompt testing: multi-part questions that require synthesis rather than direct answers. These prompts mirror the real queries where GEO matters most, giving us a direct read on whether your content is entering the retrieval pool.

Synthesis prompt test sets
Retrieval frequency tracking
Source attribution monitoring
Competitive share of retrieval reporting

Our GEO Process

From retrieval gap to retrieval presence.

Phase 01

Retrieval Gap Analysis

We build a set of synthesis-type prompts for your category and run them across major LLMs. The output tells us what sources are currently being retrieved, which of those are competitors, and what those sources have that you do not. That gap becomes the brief.

Phase 02

Content Architecture Audit

We assess your existing content cluster structure, semantic depth, and schema implementation against the sources that are currently getting retrieved. Most gaps fall into three categories: insufficient depth, poor structure for machine parsing, or missing topical coverage.

Phase 03

GEO Content Build

Content depth expansion, structured data implementation, and original research publication happen in parallel sprints. We sequence by retrieval gap size: topics where the gap is largest and where your domain has existing credibility to build on move first.

Phase 04

Monthly Retrieval Tracking

Every month we re-run the synthesis prompt set and report changes in retrieval frequency. GEO progress is measured in whether your content enters the retrieval pool more often, whether it appears alongside more authoritative sources, and whether attribution accuracy improves.

GEO in Practice

How a HR tech company went from absent to retrieved in 5 months.

The Challenge

An HR technology company published solid blog content for three years. When we ran retrieval prompts about workforce planning, employee onboarding, and HR software selection across ChatGPT and Perplexity, competitors appeared in every generated response. Our client did not appear once. The content existed. It was not retrievable. Shallow topic coverage, no structured data, no original research, and no topical authority architecture.

Our Solution

Complete GEO program: restructured 45 existing articles with semantic depth expansion, implemented Article and FAQ schema across the content cluster, published two original research pieces (survey of 500 HR managers) that became primary sources, built a topical silo around "HR technology selection" with 12 supporting cluster pages, and added structured data to all product comparison pages.

Results Achieved

0 to 38%

LLM Retrieval Rate

Synthesis prompts on target topics

+720%

Perplexity Citations

Original research pieces alone

+240%

Organic Traffic to Content Cluster

Traditional SEO lift from same work

+55%

Qualified Leads from AI-assisted research

Quarter 2 vs Quarter 1

FAQ

Generative engine optimization frequently asked questions

AEO targets being cited as the direct answer to specific questions. The buyer asks "What is the best CRM for small businesses?" and you want to be the named recommendation. GEO targets being retrieved as part of a generated response to broader topics. The buyer asks "Help me understand how to choose CRM software" and your content is woven into the generated explanation. Both matter. The difference is the type of query and how AI systems use your content.

LLMs retrieve sources that can contribute meaningfully to a generated response. Shallow content does not have enough substance to contribute. A 500-word overview of a topic competes with 3,000-word deep dives from authoritative publishers. The shallow page loses every time. GEO requires the kind of depth that gives an AI system something worth incorporating into a generated answer.

Yes, with a shared foundation. Perplexity indexes the open web broadly and retrieves based on recency, structure, and source authority. ChatGPT Browse uses Bing signals. Gemini uses the Google index with emphasis on E-E-A-T. The GEO foundation (depth, structure, original research) benefits all three. The platform-specific work happens at the edges: Bing optimization for ChatGPT, freshness signals for Perplexity, E-E-A-T depth for Gemini.

Original data is the most powerful GEO asset you can create. When you publish original survey data or original research, you become the primary source rather than a secondary cite. LLMs preferentially retrieve primary sources because they provide information not available elsewhere. Two or three well-distributed original research pieces can do more for your retrieval rate than 20 well-optimized blog posts.

Content depth expansion and structured data show results in 6-10 weeks as LLMs re-index and update their retrieval pools. Original research shows results faster, often 4-6 weeks for Perplexity, which prioritizes recent, citable sources. The full retrieval rate improvement from a complete GEO program typically materializes over 4-6 months. We track against a baseline from day one so you can see progress at each monthly reporting cycle.

Start with a GEO Assessment

Find out whether your content is being retrieved by LLMs.

We run synthesis prompts for your category across major AI systems and show you exactly what is being retrieved, why, and what it would take to get your content into that pool.

Free LLM retrieval gap analysis
Competitive source attribution report
Content architecture recommendations