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If you’ve been asked to diagnose and fix AEO gaps, the first step is to conduct an audit. For most brands, an AEO gap is any reason an AI answer engine can’t (or won’t) use its page as a source. What causes AEO gaps? Sometimes the page doesn’t exist. Or it exists, but the answer is buried in paragraph nine. Sometimes the page is fine, but the engine is still citing a G2 roundup without your company. Those are three completely different problems with three completely different fixes, and most teams treat them as one vague problem called “we’re not showing up in ChatGPT.”This guide walks through the AEO audit in order, gives you the diagnostic question for each layer, and ends with how to prioritize the fix list against actual pipeline instead of a gut feeling. Table of Contents What is an AEO content audit? How to Diagnose and Fix AEO Gaps in Coverage How to Diagnose and Fix AEO Gaps in Answerability How to Diagnose and Fix AEO Gaps in Schema Health How to Measure AI Search Visibility and Track Fixes How to Prioritize and Operationalize AEO Fixes with CRM Data Frequently Asked Questions About Diagnosing and Fixing AEO Gaps Run the audit in order. What is an AEO content audit? An AEO content audit is a structured review of potential authority: Why answer engines do or don’t use your site as a source. AEO audits evaluate content on four main criteria that AI answer engines use to determine authority. Here’s what each one covers: An AEO audit determines whether a machine can use

A vector embedding is a numerical representation created by an embedding model. The model converts text into a list of numbers that can be compared with other vectors, helping a retrieval system find passages with similar meaning even when they use different words. Semantic retrieval is one tool AI systems can use to find source material; modern retrieval can also combine semantic search with keyword search and other relevance signals. HubSpot’s State of AEO in 2026 reports that 58% of marketers say their businesses are already optimizing content for answer engines. Understanding the mechanics is useful even if you never build an embedding model yourself. Franklin Rios , CEO of Next Net, used a simple analogy for large language models (LLMs) on the Found in AI podcast : “Vectorizing is embedding information into a data format. The reason we use a data format [is] because it’s the natural language of LLMs. They consume mathematics, they consume data.” This isn’t engineering work you need to implement yourself. Instead, it changes how you think about generative engine optimization . The rest of this guide shows how passage-level retrieval, query fan-out, and AI visibility measurement translate into practical content decisions. Table of Contents TL;DR: Vector Embeddings and AEO What are vector embeddings in AEO, and why do they matter? How to Use Vector Embeddings in AEO to Power Retrieval How to Use Vector Embeddings in AEO to Structure Citable Passages How to Use Vector Embedd

AI search optimization tools help marketers understand where a brand appears in AI-generated answers, which sources earn citations, and what to improve next. They complement traditional SEO tools rather than replace them: SEO measures rankings, clicks, and organic traffic, while AI-search tooling adds visibility signals such as mentions, citations, sentiment, and answer accuracy. Search behavior now spans traditional search results and AI-generated answers. Buyers use ChatGPT, Gemini, Perplexity, Google AI Mode, and other answer engines for conversational research, so the right tool stack depends on the problem you need to solve — baseline visibility, ongoing monitoring, crawl diagnostics, first-party platform reporting, or content execution. This guide compares those jobs, the tools that fit each, and the measurable outcomes to consider when deciding what is worth paying for. How I evaluated: I work with enterprise and scaling brands on AI search visibility, and the recommendations here combine that experience with current product documentation and published research from Ahrefs, Vercel, Microsoft, Google, HubSpot, and other primary sources. Table of Contents What is AI search optimization, and why does it matter? How AI Search Optimization Differs From SEO AI Search Optimization Tools Landscape and Jobs to Be Done Make your site accessible to AI systems. Structure content for citations with schema and direct answers. Optimize content differently for ChatGPT, Gemini, Perplex

If you’ve searched “Profound versus Athena AI for AEO,” you’ve probably already hit a wall: every comparison you find is written by a competitor, an affiliate, or the vendors themselves. This is not a knock on writers who compare Profound vs. Athena AI for AEO— it’s just the nature of a fast-moving category where Profound and Athena AI, the two most-discussed answer engine optimization tools, also happen to publish their own head-to-head pages about each other. This guide is different in one specific way: every pricing figure, feature claim, and security detail below was checked directly against Profound’s and Athena AI’s (AthenaHQ’s) own pricing, product, and trust-center pages as of the date noted above, not pulled from either vendor’s marketing or from other affiliate roundups. Where a claim couldn’t be verified first-party, we say so instead of guessing. Both tools fall under the broader umbrella of AI SEO and answer engine optimization tools — software built to tell you how your brand shows up when someone asks ChatGPT, Gemini, Perplexity, or another AI engine a question, instead of typing a query into Google. If you’re new to the terminology, our AI glossary for marketers is a good primer before you dig into this Profound vs. Athena AI for AEO comparison. Table of Contents Profound vs. Athena AI for AEO at a Glance Which covers more AI engines? How do Profound and Athena AI measure visibility? Content Optimization and Workflows Team Access and Agency Fit International C

An AEO checker tells you whether the AI answers your buyers rely on actually mention your brand. People increasingly ask ChatGPT, Perplexity, and Gemini a question and act on the reply without clicking a link, so visibility that once showed up in your rankings can vanish into an answer you never see. To manage that, you need to see which questions surface your content, which reward a competitor, and where an engine gets your brand wrong. A good AEO checker measures exactly that, and the tools do it in very different ways. This guide covers what an AEO checker does, how to run manual checks across ChatGPT, Perplexity, and AI Overviews, and how the best AEO checkers compare so you can pick the right fit. Read on to discover the software that’ll help you adapt to this new era of search . Table of Contents What is answer engine optimization? What does an AEO checker do? How to Run an AEO Check in Google AI Overviews How to Check ChatGPT and Perplexity Citations AEO Checker Buyer Criteria Best AEO Checker Tools AEO Metrics that Matter From Measurement to Action in HubSpot Frequently Asked Questions About AEO Checker What is answer engine optimization? Answer engine optimization (AEO) is the practice of improving how often and how accurately your brand appears in the AI-generated answers people get from tools like ChatGPT, Perplexity, and Gemini. Read more about this shift in search behavior in our guide to generative engine optimization . Those tools are answer engines; rather tha

This Scrunch vs. Peec AI comparison evaluates how both tools measure AI answer engine representation across different buyer segments, price points, and feature scopes. This post evaluates Scrunch and Peec AI across measurement, reporting, engine coverage, optimization depth, pricing, governance, and team maturity, distinguishing verified facts from vendor claims. Table of Contents Scrunch vs. Peec AI At a Glance Scrunch vs. Peec AI By Team Maturity Scrunch vs. Peec AI: Approach and Methodology Scrunch vs. Peec AI: Monitoring Coverage and Citation Tracking Scrunch vs. Peec AI: Auditing and Optimization Scrunch vs. Peec AI: Agentic Delivery Layers Scrunch vs. Peec AI: Pricing and Plan Structure Scrunch vs. Peec AI: Attribution and Integrations Scrunch vs. Peec AI: Security and Governance Which should you choose: Scrunch vs. Peec AI? Or Choose a Workflow-Native Alternative With HubSpot AEO Frequently Asked Questions About Scrunch vs. Peec AI Scrunch vs. Peec AI At a Glance The Scrunch vs. Peec AI differences become clear fastest in three areas: scope, accessibility, and governance. Quick-Scan Checklist Choose Scrunch if your team needs: A full workflow stack: monitoring, auditing, optimization, and agentic content delivery in one platform SOC 2 Type II compliance for enterprise procurement The Agent Experience Platform (AXP), which serves AI-optimized content to crawlers at the CDN layer without touching the human-facing site SSO, RBAC, API access, and multi-brand management at

In 2025, most of the major answer engines made an almost identical update. Claude, ChatGPT, Gemini, and many others started to show what they were thinking. Answer engines would tell the user which websites it was searching, which documents it had opened, and which assumptions it was second-guessing. According to Anthropic, the company that owns Claude, the answer engine does this for three reasons . It helps people check and trust answers. It reveals mismatches between thinking and answers. It’s simply interesting to watch. But I think there’s a fourth reason that AI providers have purposefully hidden from us. The Labor Illusion In 2011, two Harvard professors published a study in Management Science that sought to understand and challenge the idea that faster means better in a customer service context. They asked 266 participants to use a travel search site. Similar to Skyscanner or Kayak, the user could enter a destination, and the site would pull back several flights to potentially book. However, there was a twist. One group saw just a plain loading wheel on a white background as the search engine filtered through results. The second saw the plain loading wheel, plus a live scrolling list of which airlines were being searched, with fares visibly stacking up as they were found. Interestingly, the wait was randomly set to 10, 20, 30, 40, 50 or 60 seconds. Once participants had seen the results, they were asked to rate the website out of seven. Turns out, the results from the

Few phenomena within the creator economy have moved as fast as AI’s embrace. What was once treated with anxious suspicion is now more widely viewed as a necessary strategy. Last year, according to Kameron Buckner , founder of Social Docket , which helps creators navigate the legal implications of their work, the conversation around AI was defined by dread. This year, she says, the tone has shifted from fear to leverage — less “this thing is going to take over” and more how do we actually use it. I spent the last several weeks talking to Buckner and others closest to this shift: top creators, consultants, founders, and social media strategists. Unsurprisingly, those who returned from the Cannes Lions International Festival of Creativity this year said AI was a centerpiece of the discourse. But across the board, I sensed no real fear. Everyone I spoke to is using AI. Some more than others, some with discernment, others with keen enthusiasm. What’s certain is that AI has brought new questions to an industry built on areas like increasing productivity, monetizing creativity, and protecting IP. The bottom line: using it wisely is the difference between staying ahead of the curve and getting drowned out by it. Table of Contents The Accelerator vs. The Author Legal is trying to catch up. Does AI level the playing field, or widen the gap? The consensus: Humans remain irreplaceable. The Accelerator vs. The Author DonYé Taylor has been the mastermind behind culture-shifting campaigns a

Most email marketing teams know the basics. Authenticate your domain. Clean your list. Write a compelling subject line—test before you send. But for enterprise and mid-market teams, doing the basics well is rarely where performance stalls. The gap shows up later — when a growing contact database starts fragmenting sender reputation, when automation workflows built for 50,000 contacts start conflicting at 500,000, when leadership asks which email campaigns actually influenced closed-won revenue, and the reporting falls silent. Email marketing challenges at scale are not beginner problems. They are infrastructure, governance, and measurement problems — and most generic advice is not written for them. This post is. Whether you are diagnosing declining inbox placement, trying to personalize at scale without a one-to-one content operation, or building the attribution model that finally connects email engagement to pipeline, the sections below give you a diagnostic framework, concrete fixes, and the right tool path to make improvements that hold. Table of Contents Why Email Marketing Challenges Get Worse at Enterprise Scale Fix email deliverability issues before they suppress growth. Solve low engagement with better targeting, timing, and testing. Reduce email production and automation challenges. Connect email marketing performance to pipeline and revenue. Use AI where it improves speed without weakening quality. 30-Day Action Plan for Improving Email Marketing Challenges Frequent

Something big just shifted in how people find answers online. More buyers are skipping the investigation and deliberation of clicking through blue links on Google, in favor of asking ChatGPT, Perplexity, and the like for one direct answer. That changes everything for us marketers. The question is no longer just “Do we rank?” It’s “Do AI search tools even mention us?” This guide breaks down what AI search tools actually are, how buyers are using them today, and which tools belong in your stack as a marketer. Table of Contents What is an AI search tool? How AI Search Tools Are Changing Buyer Research The Main Types of AI Search Tools Best AI Search Tools by Use Case How Marketers Should Evaluate an AI Search Tool Frequently Asked Questions About AI Search Tools Search no more, marketers. What is an AI search tool? An AI search tool or AI search software is any application that uses artificial intelligence to help users crawl, find, retrieve, or synthesize information. But that’s a broad net covering various tools for research, visibility, and optimization. Here’s how to tell them apart at a glance: Research: Answer engines (like ChatGPT, Perplexity, and Gemini) read your question, pull from multiple sources, and return what they think is the best answer. Sources may or may not be cited. Visibility: AI site search tools live inside a company’s own website or product. They help visitors find relevant content, answers, or products without leaving the page. Think of the search bar

Learning how to optimize your website for AI search is one of the hottest skills for marketers right now, because the audience for these tools is growing fast. Monthly unique visitors to the major answer engines climbed from 634 million in Q1 2025 to 904 million in Q1 2026, up more than 40% in a year, according to Wix Studio . But AI search, also known as answer engine optimization (AEO), hasn’t replaced SEO. The two are linked: The fundamentals that earn traditional rankings also open the door to AI citations. Since your customers use both classic search and answer engines to research their options, your business needs to show up in both. This guide is built as a repeatable framework that keeps working even as large language models (LLMs) update. I’ll cover the technical setup, the content worth quoting, the differences between engines, and how to tell whether your AI SEO strategy is actually earning traffic. Table of Contents Why SEO Still Matters for AI Search How to Optimize a Website for AI Search With People-First Content How to Optimize a Website for AI Search With Strong Technical Foundations How to Optimize a Website for AI Search With Structured Data and Snippet Controls How to Optimize a Website for AI Search With Images, Video, and Local and Product Data How to Optimize a Website for AI Search Using Q&A Formatting How to Optimize a Website for AI Search Across Perplexity and ChatGPT AI Content Optimization Workflows That Improve AI Search Visibility Myths to Ignor

How much does AEO cost? The short answer is roughly $30 a month for a monitoring tool you run yourself to over $15,000 a month for a full-service agency program that handles everything for you — with a wide middle in between. What lands you at one end of that range or the other is how much of the work you’re actually paying for. AEO pricing goes up with scope. A tool that only monitors your visibility in AI answers sits at the low end, while strategy, content production, and off-site authority building push the number up, and buying all of it as a managed service sits at the top. This guide breaks down AEO cost by approach, by budget tier, and by what a realistic pilot should run, so you can size the spend against what your team needs. Table of Contents How much does AEO cost across agencies, tools, and software? Why AEO Pricing Varies So Much AEO Pricing Models Explained What You Get at Different AEO Budget Tiers AEO vs. SEO and When You Need Both AEO Pricing Red Flags to Avoid How to Budget a 60-90 Day AEO Pilot How HubSpot AEO Fits Into the Cost Conversation Frequently Asked Questions About AEO Cost How much does AEO cost across agencies, tools, and software? AEO cost spans a wide range, from monitoring tools that start in the low tens of dollars a month (such as HubSpot AEO at $50/mo) to full-service agency programs at thousands of dollars a month (such as RevenueZen ’s $15,000 Total Market package). Agency services can cost even more for custom packages. Where you land d