When AI-generated answers began eating into traditional search traffic at scale, a lot of established SEO tools found themselves in an uncomfortable position. They were now built for a reality that was shifting under their feet. The response from most incumbents was equal parts quick and obvious: to adapt by grafting AI features onto their existing platforms.
Semrush is a well-known example of this. The SEO platform, which Adobe acquired in April 2026, launched its AI Visibility Toolkit in 2025 as an add-on and now bundles it into its Semrush One plans. While it has its merits, the product was still made by a company whose core business is traditional search, adapting to something new.
Profound was designed for something different from day one. It's the agentic marketing platform for AI search; purpose-built for Answer Engine Optimization (AEO), combining AI visibility data with an orchestrator that tells you what work needs to be done and builds the agents that do it.
Profound vs. Semrush: Original purpose and foundation
The platform you choose for AEO reflects a foundational bet: do you want a tool that was designed for AI search, or one that was designed for traditional search and forced to adapt? That distinction has important implications in the results you can expect, and the strength of the AEO program you can build.
Semrush: An SEO giant adding AI visibility to its core plans
Pros:
- 15+ years of proven SEO tooling: keyword research, backlink analysis, competitor analysis, site audits, content marketing
- AI Visibility Toolkit covers the basics: Visibility Overview, Brand Performance, Prompt Research, Competitor Research, and Prompt Tracking
- Convenient for teams already running SEO workflows inside Semrush
Cons:
- AI features were layered onto legacy SEO infrastructure, not purpose-built for answer engine behavior
- No AEO-specific content tools; its workflows serve general marketing rather than answer engine citation behavior
- AI visibility features cover surface metrics but lack the depth purpose-built platforms provide
No one can scoff at Semrush's power as an SEO platform. Over 15 years, Semrush, which Adobe acquired in April 2026, built one of the most comprehensive suites in the category, featuring keyword research across more than 28 billion terms, 43 trillion backlinks in its database, and site audit tools leveraged by teams at major companies. That foundation still merits respect.
It's on top of that foundation that Semrush's AI Visibility Toolkit sits. Features like Prompt Tracking, Brand Performance reports, Prompt Research, and Visibility Overview were added to give users a way to see where they're appearing in AI answers alongside their traditional SEO data. Any team already living inside Semrush will find that convenient, if nothing else.
The honest limitation is that convenience and depth aren't the same thing. Semrush's AI features were built to extend an SEO platform into adjacent territory, not to solve the specific problem of how answer engines discover, interpret, and cite content. On self-serve plans, what to do with the data is also largely left to the user.
Profound: Purpose-built for AEO from day one
Pros:
- Built from day one for answer engine optimization, not retrofitted from SEO
- $180M Series D at a $1.8B valuation, led by Sequoia Capital and Kleiner Perkins
- Rapid product velocity: in 2026 alone, Profound shipped AI Marketer, Projects, Context Manager, FactCheck, Ads Studio, Exa support, and 35-country Prompt Volumes coverage
Profound is a category leader in AEO, built specifically because answer engines represent a different problem than search engines. Where SEO is about ranking in a list of links, AEO is about being selected as the answer. The data models, content requirements, and measurement frameworks are different, and Profound's architecture reflects that from the ground up.
The team mirrors that focus. Semrush, now part of Adobe, is a roughly 1,500-person company where AI visibility is one of nine product areas. Profound is a focused team working on this one problem.
That focus, and the Series D behind it, show in how fast the product moves. The biggest recent release is AI Marketer (Aim), an orchestrator that tells your team what work needs to be done and builds the Agents to carry it out. It shipped alongside Context Manager, which grounds Aim in your positioning, products, audience, and strategy. Together they take Profound past reporting on AI search and into deciding and doing the work, and the same approach is starting to reach executive reporting, product pages, and social.
Reviewers consistently praise Profound for its comprehensiveness, hailing it as the best solution in the market. As one user put it, "we evaluated pretty much every serious player in the AEO/GEO space, and Profound struck the best balance between tooling depth and usability. It's not just one feature, it's a thoughtful suite that helps you identify intent keywords, execute against them, optimize content, and monitor results in one place."
Profound vs. Semrush: Real user prompt data vs. topic-level estimates
Every insight, recommendation, and content decision in an AEO tool flows from the data that powers it. The quality of that data determines whether your team is optimizing for what your audience asks, or for what a simplified model says it asks.
Semrush: A topic-level prompt database with meaningful coverage gaps
Pros:
- 317M+ prompts in the AI database sourced from real AI search clickstream data
- Prompt Research and AI Analysis reports cover ChatGPT, Gemini, Google AI Overviews, and Google AI Mode
- Brand Performance reports cover ChatGPT, Perplexity, Gemini, and Google AI Mode
- Prompt Tracking provides daily visibility for custom prompts across ChatGPT, Google AI Mode, Google AI Overviews, and Gemini
Cons:
- Volume is estimated at the topic cluster level, not per prompt, and Semrush standardizes prompt phrasing, so you see cleaned-up versions rather than raw user wording
- No demographic segmentation; no age or income breakouts on prompt data
- Brand Performance reports update weekly, not daily
Semrush's prompt database is sourced from real AI search clickstream data and Google's keyword dataset for AI Overviews; the company is transparent about this, explaining how prompt responses are captured from real user requests rather than API calls. The 317M+ figure represents a real dataset built on actual user behavior.
The more specific limitation is architectural. Semrush deliberately clusters and simplifies individual prompts into topic groups, removing duplicates and standardizing phrasing. Semrush's own documentation frames this as a feature (it keeps the database actionable rather than fragmented), but the practical effect is that volume is reported per topic, with no breakdown by audience. There's no segmentation by age or income. You get topic-level volume estimates and intent categories, not a window into what specific audience segments are asking.
Coverage is also narrower than it looks at first glance. The AI Analysis reports cover ChatGPT, Gemini, Google AI Overviews, and AI Mode. Brand Performance adds Perplexity but updates weekly. Prompt Tracking runs daily but against ChatGPT, Google AI Mode, Google AI Overviews, and Gemini only. Claude, Copilot, Grok, and DeepSeek are tracked only in Semrush's custom-priced Enterprise AIO tier, and Meta AI isn't covered at any tier.
Profound: 2B+ real user prompts with headless browser accuracy
Pros:
- 2B+ real user prompts from double-opt-in consumer panels
- Prompt Volumes breaks down by intent, country, age, and income
- Headless browser methodology captures the full front-end experience on every tracked engine, daily
- Up to nine answer engines tracked on Enterprise, including ChatGPT, Google AI Mode, Google AI Overviews, Google Gemini, Claude, Microsoft Copilot, Perplexity, and DeepSeek
Profound's Prompt Volumes feature draws from licensed AI answer engine conversations, meaning real prompts submitted by real users, aggregated from ChatGPT, Gemini, Claude, and Perplexity, then cleaned and modeled to surface usable trends. The dataset is broken down by intent (informational, commercial, conversational, and generative), age, income, country, and platform. That segmentation is what makes it useful for audience-level strategy instead of just aggregate trend-spotting.
Profound executes all prompt tracking through headless browsers, running each query through the front-end of each platform; the same experience real users get when they interact with answer engines.
As for coverage, Profound tracks up to nine answer engines on Enterprise, across 150+ regions and 30+ languages, daily. For audiences on Claude, Copilot, or DeepSeek, Semrush's self-serve plans don't show that part of the picture.
Profound vs. Semrush: Agentic layer and brand context
Semrush's AI Marketing Agent works from a marketer's brief. Profound's AI Marketer watches your AI search data and your business on its own, and presents your team with the work it has already found and ranked.
Semrush: A general marketing agent on search data
Pros:
- AI Marketing Agent helps teams create, manage, and execute campaign work end to end, from competitive research and audience personas through to channel tactics
- More than 70 AI-powered tools generate copy, images, video, audio, and code, and any generated flow can be saved as a reusable template
Cons:
- The agent builds a workflow from a person's brief, so a marketer still decides what work to start
- Its inputs are the brief, uploaded files, and web research, not measured answer engine prompt demand
- Breadth across all marketing comes at the cost of AEO-specific grounding in citation behavior
Semrush's AI Marketing Agent produces end-to-end plans with competitive research, audience personas, and channel tactics, generates the assets those campaigns need across more than seventy tools, and lets a team save any generated flow as a template to run again. For an organization already standardized on Semrush, that breadth is indisputably useful.
Still, it has limitations. The agent builds a workflow from a person's brief, so the decision about what deserves doing stays with the team. And its inputs are the brief, uploaded files, and web research, not a record of what people ask answer engines.
Profound: AI Marketer (Aim), proactive across your whole AEO program
Pros:
- AI Marketer, or Aim, orchestrates Profound's Agents: it monitors visibility, citations, sentiment, FactCheck accuracy, and competitor movement, and brings your team a ranked list of opportunities with the evidence and reasoning attached
- Those opportunities span the AEO program: a claim a model repeats incorrectly about your product, a sentiment drop on a key topic, an industry roundup that leaves you out, a publication worth pitching, a page losing citations
- Context Manager holds your positioning, products, audience, and strategy as six Brand Records, so every proposal reflects your business rather than a generic playbook
- Aim works in Slack as well as in Profound, and learns from your team's feedback, saving it in Context Manager for the next project
Cons:
- Proposals sharpen as Brand Records fill in and monitoring history accumulates, so the first weeks produce broader suggestions
Semrush's agent runs from a brief somebody wrote, but Aim works a level above that. It's a background agent that orchestrates the others: it decides which work deserves doing and which Agents to build for it, and it starts from your data. It monitors visibility, citations, sentiment, FactCheck accuracy, and competitor movement, and hands your team a ranked list of projects that deserve attention, each item with the evidence and the reasoning behind it. Accept one, and Aim breaks it into tasks and builds the Agents that execute them. All the doing is taken care of; you just have to review the output.
Context Manager works in tandem with Aim, helping it separate a problem from a problem worth your time. It organizes your identity into six Brand Records, covering Company, Market, Offerings, Audience, Strategy, and Execution, which Aim reads before proposing anything. A sentiment dip is more important when it touches the product line your strategy prioritizes. A missing roundup matters more when it reaches the audience you sell to.
Aim also learns. Your team's feedback is saved in Context Manager for the next project, and preferences you state once carry across chats and projects. Most of what Aim currently does concerns AI search, but the same approach has started extending into executive reporting, product detail pages, and social.
Profound vs. Semrush: From visibility insights to content action
Knowing you have a visibility problem and being able to act on it are two different things. This section looks at whether each platform can take a team from "here's what's missing" to "here's the published content that solves it", and how much of that journey happens inside a single platform versus across manual handoffs.
Semrush: AI-assisted content creation with SEO at the core
Pros:
- Full content workflow in one place: Topic Finder, SEO Brief Generator, AI Article Generator, AI Search Optimizer, and Content Repurposing
- AI Search Optimizer analyzes content and provides recommendations for improving visibility on both Google and AI platforms like ChatGPT
- Publishing integrations with WordPress, Mailchimp, Canva, and Google Docs
Cons:
- Content tooling runs primarily on Google and SEO data; Semrush scores drafts against factors it correlates with AI citations, but not against which pages answer engines cite for a specific prompt
- No templates built from analysis of what answer engines cite
- In self-serve plans, content performance in AI search doesn't feed back into future recommendations
Semrush's Content Toolkit now includes a full workflow, allowing users to discover topics, generate an SEO brief, draft a full article with the AI Article Generator, run it through the AI Search Optimizer for visibility recommendations, and push it to WordPress or social channels via Repurposing. It's an end-to-end pipeline, meaningful for teams who want to consolidate tooling.
The inescapable limitation is what those tools are optimized against. Semrush's content engine runs on Google keyword and topic data. Its AI Search Optimizer scores drafts against factors Semrush correlates with higher AI citation rates, but it's not analyzing which pages answer engines cite, at what depth, in response to what prompts. The optimization signal is "what makes content AI-friendly," not "what is AI rewarding right now."
That's an important distinction because a content recommendation built on citation analysis from millions of real AI responses is a different beast than one built on SEO heuristics extended to AI readiness.
Profound: Content Agents grounded in citation data
Pros:
- For a content project, Aim builds the Agents to deliver it: research against live citation data, drafting, optimization, and a publish-ready version for approval
- A template library of pre-built Agents covers more than writing, from FAQ generation and article translation to cannibalization checks, chunk-level citation audits, and internal linking
- Teams can build their own Agent in the builder, with no engineering resources required
- 16 reasoning models and deep research through Perplexity behind every Agent
For a content project, Aim builds the Agents that carry it out. Those Agents research the topic against the citations answer engines currently award on that prompt, draft against that evidence and your Brand Records, run an optimization pass, and hand you a publish-ready version for approval.
That’s one way to start an Agent. You can also pick one from the template library or build your own. The library holds several ready-made Agents, covering use cases across writing, researching, optimization, reporting, monitoring, and outreach. Profound’s content templates are built on analysis of millions of the most-cited pages, so their structure reflects what answer engines reward.
That grounding is where Profound differs from Semrush's Content Toolkit. Semrush offers a capable pipeline from topic to published article, and it optimizes for general AI-readiness. Profound's Agents optimize against what answer engines cite, prompt by prompt, and the next section covers how the results feed back into what they produce.
Profound vs. Semrush: Agent analytics and ROI attribution
AI visibility scores tell you where you stand, attribution tells you whether what you're doing is working. Without infrastructure-level tracking of AI crawler behavior and its downstream effect on traffic and conversions, AEO investment is hard to defend, and harder to improve.
Semrush: Competitor AI traffic intelligence, not own-site attribution
Pros:
- AI Traffic Dashboard tracks AI-referred human traffic
- Trending Pages surface which competitor URLs are receiving the most AI-driven traffic
Cons:
- On self-serve plans, AI traffic data is estimated from Semrush's clickstream panel
- Self-serve plans don't show which AI crawlers access your content; log-file bot tracking is reserved for Semrush Enterprise
- No connection between AI traffic trends and content recommendations; insights from the dashboard don't feed back into the platform's content layer
Semrush's AI Traffic Dashboard shows you which domains are receiving human traffic referred from AI assistants, which AI platforms are driving that traffic, how it's trending over time, and which pages are earning it. It's a useful lens for teams who want to understand how competitors are winning AI-referred traffic.
The limitation is what it measures and whose data it uses. The AI Traffic Dashboard is built on Semrush's clickstream panel, the same methodology behind Traffic Analytics, estimating traffic based on modeled user behavior rather than direct measurement. It shows AI-referred human visits arriving at competitor domains. On self-serve plans, it doesn't show AI crawlers accessing your own infrastructure (Semrush Enterprise adds log-file bot tracking), which pages they're reading, how often they return, or how that behavior changes after you publish new content. That's a different problem, and one the AI Traffic Dashboard wasn't designed to solve.
Profound: CDN-level attribution that closes the feedback loop
Pros:
- Agent Analytics integrates at the CDN/server layer for infrastructure-level AI crawler tracking
- GA4 integration measures how AI search drives human traffic and conversions
- Real-time visibility into which AI bots access your content, how often, and which pages they prefer
- Agent Analytics and the content layer are architecturally connected: crawler behavior data feeds directly into content recommendations
Cons:
- Agent Analytics setup requires a CDN or server-log integration, which involves a technical implementation step
Profound's Agent Analytics operates at the infrastructure layer. Rather than client-side JavaScript tracking, it pulls request data directly from the CDN or server to identify and classify AI-originating traffic. Integrations cover the full stack: Akamai, AWS CloudFront, Cloudflare (Worker and Logpush), Fastly, Google Cloud Platform, Netlify, Vercel, and WordPress. GA4 integration ties this to downstream human traffic, so teams can see not just which crawlers are reading their content, but how AI search is driving human visitors and conversions.
The more important distinction is architectural. In Profound, Agent Analytics and the content creation layer aren't two separate tools that happen to share a platform. Crawler behavior data routes back into content recommendations directly: which pages AI bots access, which content earns citations, and how citation rates change after publishing new content are all inputs into what Aim suggests creating next and what Agents produce.
Profound vs. Semrush: The final verdict
Semrush is an SEO titan that deserves its flowers in that category. For keyword research, backlink analysis, site audits, and rank tracking, it remains a top choice, and many Profound customers continue to use it for exactly those purposes.
But stack it up against Profound, and the places where the platforms diverge are sharp and specific. Semrush added an AI visibility toolkit to an SEO suite. Profound is an agentic marketing platform for AI search, a different category of product, and the only one of the two that covers everything an AEO program needs:
- A data foundation built on 2B+ real user prompts broken down by demographics
- Headless browser methodology that captures what users actually see
- Aim, Profound's orchestration layer, brings your team ranked opportunities across content, accuracy, sentiment, and earned media
- Context Manager, which holds your positioning, products, audience, and strategy
- Agents that do the work for you
- CDN-level Agent Analytics that closes the loop between content and citations, so each cycle is sharper than the last
- FactCheck, which flags inaccurate claims AI models make about your brand and the sources repeating them
And more to come as the space continues to evolve.
If AEO is a priority for your brand, book a demo with our team. See how Profound compares to Semrush's AI Visibility Toolkit; side by side, on your own prompts and topics.
Profound vs. Semrush FAQs
What is the main difference between Profound and Semrush for AEO?
Semrush is an SEO platform, now owned by Adobe, that added AI visibility, first as an add-on and now bundled into its Semrush One plans. Profound is an agentic marketing platform built for Answer Engine Optimization from the ground up. Two differences follow. The first is data: Semrush estimates volume at the topic level from clickstream data, while Profound draws on 2B+ real user prompts with prompt-level and demographic detail. The second is who starts the work. Semrush's AI Marketing Agent runs from a marketer's brief. Profound's AI Marketer (Aim), a background agent that orchestrates the work, monitors your AI search data, reasons from the brand knowledge Context Manager holds about your business, and brings your team ranked opportunities, then builds the Agents that carry them out.
Can I use Semrush and Profound together?
Yes, and many teams do. The most natural split is Semrush for traditional SEO (keyword research, backlink analysis, site audits, rank tracking) and Profound for AEO (AI visibility monitoring, content creation and optimization for answer engines, Agent Analytics, and attribution). The two platforms aren't doing the same job, and for teams that need both SEO and AEO coverage, running them in parallel is a common setup.
Does Semrush offer content workflows for AI search optimization?
Not in the AEO-specific sense. Semrush's Content Toolkit covers topic research, SEO briefs, AI article generation, an AI Search Optimizer, and repurposing, and its AI Marketing Agent can save any flow as a reusable template. What's missing is the AEO grounding. Its optimization scores drafts against factors correlated with AI citations rather than what answer engines cite prompt by prompt, and on self-serve plans content performance in AI search doesn't feed back into its recommendations. Profound's Agents, whether Aim builds them, a marketer builds them, or they come from the template library, work from live citation data, and Agent Analytics feeds the results into the next round.
Which platform is better for enterprise brands focused on AI search?
Profound. Enterprise teams need more than a visibility dashboard: they need to act on insights at scale, measure whether that action is working, and demonstrate ROI to leadership. Semrush's self-serve AI features are largely self-directed, and infrastructure-level AI bot tracking is limited to its Enterprise tier. Brands like Ramp, Figma, Walmart, and MongoDB run their AEO programs on Profound.