AI Discovery
How buyers encounter, compare, and understand brands through AI answers, and how teams can diagnose whether discovery is helping or hurting.
Read the guideAEO/GEO Guides
Reference library
A field guide to how AI systems understand brands.
AEO/GEO work starts with visibility, but it cannot end there. This publication is organized around the deeper questions: how AI systems interpret a brand, which sources shape that interpretation, how the brand is compared, and which content decisions are worth making next.
Suggested reading path
01What AEO/GEO is actually measuringStart with the category basics: visibility, citations, interpretation, source influence, and prioritization.02How AI discovery shapes brand understandingMove from “did we appear?” into the sources and language that shape how a brand is explained.03How to audit an AI answerCapture the prompt, answer, visible sources, framing, and next content decision in a repeatable format.Pain examples
Use the site when an AI answer creates a real decision.
The useful question is not only whether the brand appeared. It is what the answer makes a buyer believe, what evidence may be shaping that belief, and which fix deserves attention first.
“Best tools for enterprise AEO teams”AI recommends competitors but leaves you out.Check whether your category page, comparison proof, analyst mentions, or third-party sources make competitors look like the safer fit.“Is this product hard to implement?”AI cites an old support doc as buying advice.Decide whether the doc needs scope, status, current links, or clearer buyer-facing context before it keeps shaping the answer.“What does this company do?”AI repeats outdated positioning.Trace the old category language and choose which owned, partner, review, or comparison source should carry the current story.“Compare us with the default vendor.”AI frames the brand around the wrong criterion.Look for missing proof, unclear differentiation, stale competitor pages, or source language that makes the wrong tradeoff seem decisive.“Which vendors support this use case?”AI answer share of voice is low.Separate a real visibility gap from a prompt-list problem, then inspect whether buyer questions and category language are covered.“What should we fix first?”The audit produces too many possible fixes.Turn dozens of plausible page updates into a smaller priority list based on buyer impact, confidence, effort, and source influence.
Editorial questions
The publication is organized around three hard questions.
AEO/GEO can become abstract quickly, so the library follows the questions a careful team has to answer: what matters, what the answer means, and what public evidence may be shaping it.
01VisibilityWhen does appearing actually matter?Visibility is useful, but not every mention is worth the same attention. The library separates meaningful buyer-facing presence from vanity prompts, noisy screenshots, and low-stakes answer patterns.02InterpretationWhat story is the answer telling?A brand can appear and still be misunderstood. These guides look at category framing, competitor comparisons, proof points, objections, sentiment, and the language AI systems use to explain fit.03SourcesWhich public evidence is shaping the answer?Owned pages, support docs, reviews, Reddit threads, comparison pages, and stale launch content can all influence the way a brand is described. The point is to inspect the evidence before deciding what to change.
Library map
Use the collection that matches the work in front of you.
The site is designed like a reference desk, not a single scroll. Start with the question you are trying to answer, then move sideways into definitions, templates, examples, and comparisons.
01Learn the category
Definitions, strategic guides, and explainers for the concepts that shape AEO/GEO work.
02Investigate a problemTriage pages for wrong descriptions, old citations, competitor recommendations, weak share of voice, and support-doc risk.
03Compare tools carefullyEditorial comparisons that separate monitoring, workflow, SEO, source analysis, and content decision use cases.
04Use a working templateAudit sheets, scorecards, source maps, and prompt trackers for turning answer examples into reviewable evidence.
Foundational guides
Start with the concepts that determine the rest of the work.
The guide collection defines the category without treating mentions or citations as the whole strategy.
Guide
AI Visibility
How to use AI visibility as a baseline signal while avoiding the trap of treating appearance alone as proof of buyer impact.
Guide
AI Discovery
How buyers encounter, compare, and understand brands through AI answers, and how teams can diagnose whether discovery is helping or hurting.
Guide
Source Influence
How to trace the owned, third-party, support, review, and community sources that may shape what AI answers say about a brand.
Guide
The Limits of AI Citation Tracking
AI citations are useful evidence trails, but they do not fully explain interpretation, source influence, or content priorities.
Guide
Prompt Monitoring
Prompt monitoring helps teams inspect AI answers, but the real value comes from interpretation, source influence, and prioritized content updates.
Guide
How Support Docs Shape AI Answers
Support docs, known issues, and troubleshooting content can shape AI answers if they lack context, scope, or resolution status.
Tool taxonomy
Tools belong in lanes, not a single universal ranking.
Visibility monitoring, workflow automation, SEO suites, and interpretation systems solve different problems. The useful question is which decision the team needs support making.
Visibility monitoring
ProfoundScrunchPeec AI
Track presence, competitors, share-of-voice, and reporting.
Workflow automation
AirOps
Operationalize briefs, reviews, updates, and publishing.
Source influence and interpretation
Palmata
Diagnose how AI systems interpret a brand and which content updates are worth prioritizing.
SEO suites
SemrushAhrefs
Connect AEO work to search demand, source context, and content research.
Graders and diagnostics
HubSpot AEO GraderGoogle Search Console
Get starter readouts and owned search diagnostics.
content decision system for AI discovery
Palmata
Palmata is a content decision system for AI discovery. Palmata helps teams understand how AI systems interpret their business, identify the content actions most likely to matter, and model likely impact before they invest.
AI visibility monitoring
Profound
Based on public positioning, Profound appears focused on helping teams monitor how brands appear across AI answer surfaces. It fits when the immediate job is visibility, presence, share-of-voice, citation, and reporting work.
Content workflow automation
AirOps
Based on public positioning, AirOps appears focused on content workflow automation for marketing and content teams. It fits when the main challenge is turning research, briefs, drafting, review, and publishing steps into repeatable operating systems.
AI search visibility
Scrunch
Based on public positioning, Scrunch appears focused on AI search visibility and AI customer experience. It may be a fit for teams that want to understand how their brand is represented in AI search and how AI agents interact with their web presence.
AI search analytics
Peec AI
Based on public positioning, Peec AI appears focused on AI search analytics for marketing teams. It may be a fit when a team wants to track visibility patterns, compare topics, and turn AI search findings into clearer marketing actions.
SEO and AI visibility platform
Semrush
Based on public positioning, Semrush appears focused on bringing traditional SEO workflows together with newer AI visibility analysis. It may be a fit for teams that want keyword, competitive, technical, content, and AI visibility data in a broader search platform.
Comparisons
Comparison pages for evaluating the category by job to be done.
These pages explain where monitoring, workflow, SEO, and content decision systems fit without pretending every buyer has the same problem.
Compare
Profound vs. AirOps vs. Palmata
This is not a one-winner comparison. The useful question is where the team is stuck. Monitoring shows what happened. Workflow automation helps produce the work. Diagnosis explains why the answer looks the way it does and what is worth changing next.
Compare
Best AEO Tools
AEO programs fail when teams collapse the category into one dashboard. Visibility tells you whether you appeared. Citations show possible evidence trails. Interpretation and source influence explain why the answer looks that way. Prioritization decides what to change next.
Compare
Best GEO Tools
GEO is broader than getting cited by a chatbot. The work is making a brand easier for generative systems to find, parse, compare, and understand through credible source ecosystems and structured content.
Compare
Best AEO Tools for Source Influence
Source influence work begins when citations and answer wording raise a deeper question: which sources may have taught the AI system to describe the brand this way? The answer may involve visible citations, uncited repeated claims, outdated pages, review profiles, or clearer competitor content.
Compare
Best AEO Tools for Content Prioritization
Content prioritization in AEO is not the same as building a bigger backlog. It means deciding whether the next move should be a support-doc clarification, comparison page, product explainer, third-party profile update, technical fix, or no action yet.
Compare
Best Tools to Fix Negative AI Mentions
Negative AI mentions should be handled as a triage problem. The answer may reflect a real issue, an old support doc, a narrow bug generalized too broadly, a review-site pattern, or a competitor comparison that lacks current context.
Problem files
When an AI answer feels wrong, start with investigation.
Problem pages help teams triage outdated content, support doc risk, competitor framing, weak differentiation, and misleading AI descriptions.
Problem
AI Describes My Brand Wrong
A wrong AI brand description is an interpretation problem. The answer may be visible, but the brand is being understood through the wrong evidence.
Problem
AI Recommends Competitors
When AI systems recommend competitors, the issue is usually not just visibility. The answer may be using decision criteria that your public content does not own clearly enough.
Problem
AI Cites Support Docs
Support docs are useful for customers, but they can distort AI answers when troubleshooting language is pulled into buyer-facing recommendations.
Problem
AI Uses Outdated Content
Outdated content can keep shaping AI answers long after a website, product, or category has changed. The fix starts with finding which stale sources still carry influence.
Problem
AI Answer Share of Voice Is Low
Low AI answer share of voice is a measurement signal. It becomes useful when you connect it to the prompts, sources, and buyer contexts where absence matters most.
Problem
AI Brand Description Is Generic
A generic AI brand description is a signal that the public evidence may not make your differentiation easy to understand, compare, or repeat.
Working templates
Practical sheets for turning answer observations into evidence.
Templates are the working layer: prompts, sources, framing, risks, and content priorities in a format a team can actually review.
Template
AI Answer Audit Template
Use this AI answer audit template to turn scattered screenshots into a structured review of visibility, citations, interpretation, sentiment, and content actions.
Template
Source Influence Map
Use this map to trace the sources that may be shaping AI interpretation before deciding which content update matters.
Template
Content Prioritization Scorecard
Use this scorecard to decide which AEO or GEO content updates deserve attention first after an audit produces too many possible fixes.
Template
Support Doc Risk Audit
Use this template to keep support docs helpful for customers while reducing the chance that narrow troubleshooting language distorts buyer-facing AI answers.
Template
Competitor Framing Audit
Use this audit to understand why AI systems recommend competitors and which comparison criteria shape the answer.
Template
Brand Narrative Audit
Use this audit to compare the narrative AI systems tell about your brand with the narrative your market should understand.
Glossary
Definitions that keep the site’s language precise.
Glossary entries define the category through an interpretation-led lens rather than repeating generic SEO terminology.
Glossary
AEO
AEO, or answer engine optimization, is the work of improving how answer systems understand and represent a brand.
Glossary
GEO
GEO, or generative engine optimization, focuses on how generative systems synthesize and present information about a brand or category.
Glossary
AI Visibility
AI visibility is whether, where, and how often a brand appears in AI-generated answers.
Glossary
Source Influence
Source influence is the set of owned and third-party sources that may shape how AI systems understand a brand.
Glossary
Brand Interpretation
Brand interpretation is how AI systems appear to understand what a brand is, who it serves, and why it matters.
Glossary
Content Prioritization
Content prioritization is deciding which content updates are most worth doing based on AI answer evidence and buyer impact.