After a few years of AI search upending how people find information, the market is beginning to separate tools that genuinely help from those that don't. For brands serious about showing up in ChatGPT, Claude, & co., the question is no longer "should we invest in AI visibility?", but "which platform do we build on?"
Two names that come up frequently in that conversation are Profound and Scrunch. It’s worth knowing up front that Sitecore acquired Scrunch in June 2026 and is integrating it into its digital experience platform. Scrunch still sells as a standalone product with public pricing, but its roadmap now belongs to Sitecore, which is integrating it with SitecoreAI. Buyers should weigh how much future investment will be aimed at Sitecore customers.
Scrunch is a capable tool, particularly for teams getting started with AI visibility or already running Sitecore. It monitors well, audits sites for AI readiness, and serves agent-friendly versions of your pages through its Agent Experience Platform. Profound is an agentic marketing platform purpose-built for the AI era, with an orchestrator that decides what work matters and builds the Agents that do it.
For enterprise brands that need deep data, agentic execution, and a strategic partnership model, these platforms aren't in the same category.
Profound vs. Scrunch: Data foundation and prompt intelligence
The success of an Answer Engine Optimization strategy hinges on quality data. You can have the best monitoring dashboard the market has to offer, but if the underlying prompt intelligence is imprecise, you're building a content strategy on assumptions. This is the biggest divergence point between Scrunch and Profound.
Scrunch: Topic-level volume estimates from third-party data
Pros:
- Uses both browser automation and official platform APIs, with response validation against a continuously updated dataset
- AI Search Trends feature gives teams directional signals on which themes are gaining momentum
- Prompt setup is flexible: import via CSV, convert from SEO keywords, or use AI-generated suggestions
Cons:
- Volume data is topic-level, plus an alpha keyword estimator; nothing at the prompt level
- Scrunch's real-interaction panel data only surfaces at the topic level; tracked prompts are still chosen by the team
- Teams can't identify which specific prompts carry real-world demand
- Prompt-level search volume measurement isn't currently offered
Scrunch's AI Search Trends estimates AI search volume at the topic level using third-party data. That means teams can see which broad themes are gaining traction across AI platforms, but not which specific prompts within those themes are generating user demand, which inevitably affects content optimization decisions.
The prompt setup process compounds the issue. Scrunch monitors the prompts teams configure, which can be imported from CSV, converted from existing SEO keywords, or drawn from Scrunch's AI-generated suggestions. All of these are reasonable starting points, but they share a common limitation: they're hypotheses about what people are asking, not the questions people are actually asking. A team that migrates its keyword list into Scrunch and calls it a prompt strategy has essentially mapped SEO assumptions onto an AI visibility tool. The monitoring will run, the data will accumulate, and the reports will look complete, but the prompts being tracked may bear only a loose relationship to the real conversations happening inside AI engines.
Scrunch validates responses against a continuously updated dataset and uses browser automation alongside official platform APIs, which adds some rigor to the response-collection side of the equation. The data quality concern isn't about how Scrunch reads AI outputs; it's about whether the inputs (the prompts being tracked) reflect actual user behavior. On that question, the platform's own description of its volume data as "directional, not exact" is the clearest signal of the ceiling.
Profound: 2B+ real user prompts
Pros:
- 2B+ real user prompts sourced from real conversations with answer engines
- Prompt Volumes breaks down demand by intent (informational, commercial, conversational, and generative), age, income, country, and platform
- Prompt Volumes, refreshed weekly, surfaces what real users are asking, revealing topics and phrasing that traditional keyword research misses entirely
- Prompts are run through the front-end browser, not via API, mirroring exactly what real users see
Cons:
- The depth of the dataset means there's a learning curve in knowing which dimensions (intent, country, demographic) to prioritize first
The foundation of Profound's data advantage is 2B+ real user prompts; actual conversations people are having with answer engines, licensed from double-opt-in consumer panels and modeled to correct for demographic and geographic bias, rather than topic-level proxies.
Prompt Volumes surfaces the specific phrasing, adjacent topics, and intent patterns that traditional keyword research never captures. From there, those prompts can be broken down by intent, platform, country, and demographic factors including age and income, meaning the same prompt can tell a completely different strategic story depending on which audience is driving the volume. One reviewer described Prompt Volumes as "immensely helpful as it shows how often certain prompts are being searched for and the size of opportunities different themes present."
As for the data collection methodology, Profound runs prompts through the front-end browser instead of API calls. This distinction is less technical than it sounds, but has far-reaching implications on the quality of your data. API responses and front-end responses from the same LLM can differ because the front-end experience reflects the full model behavior that real users encounter, including any interface-level context. Running prompts the way users actually run them means the visibility data reflects what's really happening, not an approximation of it.
Profound vs. Scrunch: Agentic layer and brand context
Scrunch built a platform that presents your content to AI agents in the form they prefer. Profound built an orchestrator for your marketing team that decides which projects matter across content, accuracy, sentiment, and earned media, then builds agents to carry them out.
Scrunch: Built to serve agents, with execution led by your team
Pros:
- The Agent Experience Platform serves AI-optimized versions of your content directly to AI agents at the CDN layer, without altering what human visitors see
- Optimizer draws on Site Optimization for data-backed recommendations on what to update or create, and Knowledge Studio pulls from approved internal sources so output stays true to your brand
- Scrunch MCP, available on all plans, lets teams query their data and take actions such as tracking new prompts and competitors from Claude, ChatGPT, or Copilot
Cons:
- General-purpose drafting of new human-facing content is not in the product today
- Signals flags changes and suggests next steps, but a person still decides what becomes work
- AXP’s end-to-end workflow shipped between July and September 2026, so it has a short track record, and its roadmap now sits inside Sitecore
AXP is an interesting piece of engineering. Serving AI-optimized content straight to agents at the CDN layer, while human visitors continue to see the page you designed for them, is a real architectural idea rather than a feature bullet. Optimizer and Knowledge Studio back it up, pairing diagnostics-led recommendations with approved internal sources so output stays close to your material, and Scrunch MCP lets a team work with the data from the AI assistant it already uses.
However, there are considerable limits here. General-purpose drafting of new human-facing content isn’t in the product, so the heavier work still happens somewhere else, in Sitecore’s content tools for Sitecore customers, and in other tools for everyone else. And while Signals flags changes on its own, Site Optimization and Optimizer produce recommendations, and a person decides which ones become a given month’s plan.
Profound: AI Marketer (Aim), an orchestrator on your side of the exchange
Pros:
- AI Marketer (Aim) reads visibility, citation, sentiment, accuracy, and competitor data and proposes the projects most worth your team's time, evidence included
- Projects span content, accuracy corrections, sentiment, earned media, and customer questions
- Context Manager holds positioning, products, audience, and strategy in six Brand Records that shape what Aim proposes
- Aim builds the Agents that carry out an accepted project; a person approves before anything publishes
- Aim learns from your team's feedback, saving it in Context Manager for the next project
Cons:
- Aim proposes more precisely once Brand Records and monitoring history build up
Aim watches the signals Scrunch reports on, plus sentiment, FactCheck accuracy, and competitor movement, and turns them into a ranked list of projects, each with the evidence and reasoning behind it.
Projects span a wide range of issues, from correcting an outdated product claim to responding to a sentiment drop traced to a review site, winning back a prompt that a competitor just took, and more. Once you accept a project, the rest of the work is out of your hands. Aim breaks it down into tasks and builds the agents that execute them; all you have to do is review the finished product.
Knowledge Studio and Context Manager both keep work close to your brand, and they apply at different points. Knowledge Studio grounds output in approved internal sources once a person has chosen what to optimize. Context Manager organizes positioning, product details, audience, and strategy into six Brand Records, which Aim reads when deciding what to propose, so brand knowledge also influences project rankings.
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. Its remit today is mostly AEO, but it’s starting to extend to executive reports, product detail pages, and social posts adapted from one asset.
Profound vs. Scrunch: From insights to action
Visibility data has little value sitting in a dashboard. The measure of an AEO platform isn't just how clearly it shows you where you stand, but how directly it helps you influence it. Both Scrunch and Profound have moved beyond pure monitoring, but what "taking action" means inside each platform is quite different.
Scrunch: Monitoring and recommendations with limited execution tools
Pros:
- Insights surfaces optimization guidance, citation analysis, and crawl errors
- Page audits and Optimizer recommendations show what to update or create
- For Sitecore customers, insights now feed Sitecore's content management and content marketing workflows
Cons:
- No general-purpose drafting of new human-facing content inside Scrunch
- No library of ready-made content or agent templates for AEO work
- Outside Sitecore, the gap between insight and published content remains largely manual
Scrunch's monitoring and auditing capabilities are solid. The dashboard gives teams a clear picture of brand presence across AI platforms, citation trends, and how they stack up against competitors. Insights goes a step further by surfacing specific recommendations and crawl errors rather than leaving teams to draw their own conclusions from raw data.
The execution layer depends on what else you run. Sitecore has integrated Scrunch with SitecoreAI's content management, with asset management in early access, so a Sitecore customer can move from diagnosis to editing without switching platforms. For everyone else, acting on Scrunch's findings means taking the insight to another tool and doing the work there. For a small team running a handful of pages, that's manageable. For an enterprise team optimizing across dozens of topic clusters, the handoff adds up fast.
Profound: Agents that do the work and learn from it
Pros:
- Each content project Aim proposes comes with Agents that research, draft against your Brand Records, optimize, and return work ready for approval
- Templates reach past articles into cannibalization checks, analyst report simulations, sentiment tracking, and media lists
- Any team member can build a custom Agent without technical expertise
- 16 reasoning models plus deep research capabilities power the work, not generic outputs
- Answer Engine Insights and Agent Analytics record which LLMs cite which pages and feed that back into what Aim proposes
Cons:
- The full value of the feedback loop requires time and published content volume to accumulate meaningful citation data
Agents are responsible for execution. Mostly, Aim builds them specifically to deliver on a project, but users can also assemble Agents outside of that scope. You have two options here: either build the Agent yourself, with the intuitive drag-and-drop builder, or ask Aim to build it for you.
Profound also offers an extensive library of templates covering recurring jobs, and its content templates are built on analysis of millions of pages answer engines currently cite. Next to brief and article drafting, you can also find Agents for content refreshes, FAQ generation, outreach support, internal linking, and more.
After publication, Profound tracks whether AI crawlers access each piece, whether answer engines cite it, and for which prompts. That data feeds back into what Aim proposes and what the Agents produce, so the more you publish, the more precisely the next project targets where you're most likely to win.
Users heap praise on Agents, and have enthusiastically adopted the feature across the entire organization. One reviewer noted how Agents gave their team the ability to "translate insights into scalable, repeatable strategic action" rather than treating each piece of content as a one-off exercise. Another described how "Profound has become a cornerstone of our marketing strategy," adding that its agentic workflows let the team "move beyond manual tasks."
Profound vs. Scrunch: Agent analytics and ROI attribution
Publishing AEO-optimized content is a starting point, not an outcome. The harder question is whether that content is being picked up by AI systems, and whether the investment is producing anything a CFO would recognize as a result.
Scrunch: AI bot traffic monitoring with CDN integrations
Pros:
- Tracks AI bot activity through direct CDN and hosting integrations
- Distinguishes between training bots, indexer bots, and retrieval bots
- Tracks AI referral traffic alongside bot activity, giving teams a side-by-side view of human visitors arriving from AI platforms versus AI crawlers reading the content directly
Cons:
- Site Optimization ranks page fixes from crawl behavior, but crawler data doesn't feed new human-facing content
- Accuracy checking sits in Knowledge Studio, which is still in beta and limited to Enterprise plans
- Conversion and revenue reporting runs through GA4 and depends on how GA4 is set up
Scrunch's bot tracking is useful, providing insight into which AI platforms are crawling your site, how often, and what they're prioritizing. The distinction between training bots, indexer bots, and retrieval bots is especially practical: a retrieval bot accessing a page suggests that content is in active consideration for AI responses; a training bot visit is a different signal entirely, with different implications for what to do next.
The AI referral traffic view adds another layer, as teams can see not just which bots are reading their content, but which AI platforms are sending human visitors as a result.
Scrunch has started closing this loop: Site Optimization reads how agents crawl your site and ranks page fixes by impact, and Agent Pages serves the fixed versions to agents. What it doesn't do is turn those findings into new human-facing content or broader projects; that still happens in another tool, or in SitecoreAI for Sitecore customers.
Profound: CDN-level attribution with a self-improving feedback loop
Pros:
- Agent Analytics uses CDN-level integrations (Akamai, AWS, Cloudflare, Fastly, and more) plus GA4 to track AI crawler behavior and its influence on human traffic in one place
- Teams can see which pages AI models prefer, identify technical barriers blocking crawler access, and submit content directly to address those gaps
- Crawler behavior data feeds back into content recommendations; the analytics and the content layer are connected by design
- GA4 connects AI visibility to human traffic and conversions, giving enterprise teams a credible narrative for leadership reporting
Cons:
- Full GA4 integration setup requires some technical configuration, which adds onboarding time for teams without dedicated technical resources
- Attribution clarity improves over time as content volume and crawler data accumulate, so newer accounts see less signal
Profound's Agent Analytics answer the question that comes after the monitoring dashboard and optimization efforts: did any of this work?
CDN-level integrations with Akamai, AWS, Cloudflare, Fastly, and others mean Profound can see precisely when AI crawlers access content, which pages they prioritize, and how often they return. The GA4 extension adds the human traffic dimension, bringing sessions, key events, and revenue into the same view as crawler and citation data, so you can connect AI crawler activity and citations to the traffic and conversions that follow. That's a measurement story that holds up in a budget conversation.
The architectural difference from Scrunch is that none of this lives in isolation. When Agent Analytics shows that retrieval bots consistently access a page that never surfaces in citations, Aim turns it into an optimization project. When a page earns citations across multiple LLMs, that pattern informs the structures the Agents use next.
The loop is closed by design: observe crawler behavior, adjust content strategy, measure citation impact, repeat. Teams don't have to manually connect those dots because Profound is designed on the assumption that they're the same workflow.
Profound vs. Scrunch: Strategic partnership, support, and guidance
In a discipline where so many companies are still working out the fundamentals, the support model behind an AEO platform is often the difference between a tool that's used strategically and one that’s used to produce reports nobody acts on.
Scrunch: Self-serve with tiered enterprise support
Pros:
- Enterprise plan includes email and Slack support, strategy reviews, custom onboarding, quarterly executive support, and a dedicated GTM team
- Agency Partner Program is well-developed: up to 20% referral commission, prospecting licenses, dedicated GTM support, and co-marketing funds, a strong model for agencies building AEO into their service offering
Cons:
- Starter relies on email support and a 30-minute onboarding session
- Strategy reviews start on Growth, but Slack support and a dedicated GTM team are reserved for Enterprise
- Self-serve onboarding in a new discipline means teams bear the cost of trial and error themselves
Scrunch's tiered support model is straightforward: get started on Starter or Growth, scale to Enterprise when you need more hands-on guidance. For teams with existing AEO expertise who primarily need a capable monitoring tool, the self-serve path is fine.
The Agency Partner Program is genuinely well-constructed, with referral commissions, prospecting licenses, and co-marketing support giving agencies good incentive and infrastructure to build Scrunch into client engagements.
The limitation surfaces for brands that are newer to AEO and don't yet have in-house expertise. Learning a new discipline and a new platform simultaneously, on email support and periodic strategy reviews at best, is a slow and expensive way to find your footing.
Mistakes made in the early months of an AEO program (wrong prompt selection, misread visibility data, content that doesn't match actual user intent) compound over time. The self-serve model puts the cost of those mistakes entirely on the customer.
Profound: Dedicated support for Enterprise teams
Pros:
- Enterprise customers get dedicated support, with the option of a dedicated engagement manager
- Dedicated Slack channel and a specialist on a 24-hour SLA for Enterprise accounts
- Support operates as a strategic extension of the customer's team: competitive intelligence sharing, tailored AEO strategy, content prioritization guidance
- Profound University provides structured training resources for teams building internal AEO capability
- Support proactively flags platform updates, new LLM developments, and strategic opportunities customers may not yet know to ask about
Cons:
- The depth of the support model is built into Profound's pricing, which sits meaningfully above Scrunch
- High-touch support works best when customer teams are engaged; teams that want a hands-off tool relationship may not fully use what's available
Profound's support model was conceived around a simple premise: most marketing teams don't have an AEO expert on staff yet, and the platform is only valuable if it’s used strategically. As such, Enterprise customers get dedicated support, including the option of a dedicated engagement manager, providing, as Ronak Patel, Head of Marketing at CRS, explains "strategic counsel on how to adapt as answer engines evolve and how to optimize our content for LLMs."
That's the intended experience, and the G2 reviews reveal it's the actual experience too. Multiple users describe the support relationship in almost identical terms: it feels like an extension of the team, not a vendor relationship.
What makes this more than a service promise is the expertise behind it. Profound's team members share competitive intelligence, help customers identify which prompts and content gaps will have the highest impact, and adapt recommendations as the AEO landscape shifts. The Profound University adds a self-serve training layer for teams that want to build internal capability alongside that guidance.
Profound vs. Scrunch: Final verdict
Scrunch is a legitimate AEO platform, with clean and organized monitoring dashboards, good competitive benchmarking, and AXP as a technically differentiated feature. It's also no longer an independent company. Sitecore acquired Scrunch in June 2026 and is integrating it with SitecoreAI. At $250/month for the Starter plan (billed annually, as of this writing), it's an accessible entry point for teams that primarily need AI visibility reporting and are comfortable acting on insights in other tools.
The more relevant question for a buyer today is whether they want a standalone, purpose-built AEO platform or one whose roadmap is now tied to Sitecore's digital experience suite.
Profound represents the former, an agentic marketing platform purpose-built for enterprises:
- The 2B+ real user prompts powering Prompt Volumes give content teams demand signal that no topic-level estimate can replicate
- Aim, the orchestrator that runs Profound's Agents, brings your team ranked projects across content, accuracy, sentiment, and earned media, grounded in the six Brand Records Context Manager holds about your business
- Agent Analytics closes the loop between AI crawler behavior and content recommendations, so the platform grows smarter over time
- The company is propped up by a fast-growing team of engineers and AI researchers, and a $180M Series D at a $1.8B valuation.
Scrunch offers an enterprise team better instrumentation, now backed by Sitecore's resources rather than a standalone startup's. Profound gives them a system that acts on the instruments.
If you're ready to build a mature program that scales alongside your company, book a demo with our team.
Profound vs. Scrunch FAQs
What's the main difference between Profound and Scrunch?
Both Scrunch and Profound monitor brand visibility across AI answer engines, but they diverge in data depth, in what happens after the report, and now in corporate structure. Scrunch offers solid monitoring dashboards, site auditing, an MCP server, and its Agent Experience Platform for serving AI-optimized content to AI agents, and as of June 2026 it's owned by Sitecore and being integrated with SitecoreAI. Profound is an independent agentic marketing platform: 2B+ real user prompts underneath the insights, Aim, a background agent that orchestrates the work, bringing ranked projects across content, accuracy, sentiment, and earned media, Context Manager grounding them in your brand, Agents carrying them out, and Agent Analytics measuring which pages actually earned citations. Scrunch reports the channel and optimizes how agents read your site. Profound runs the program.
Which platform is better for enterprise brands, Profound or Scrunch?
Profound is purpose-built for enterprise scale. It supports up to nine answer engines, custom prompt tracking, 30+ languages, SOC 2 Type II, an independent HIPAA assessment, SSO/SAML, and dedicated Enterprise support with a Slack channel and a 24-hour SLA.
Scrunch is SOC 2 Type II audited and GDPR/CCPA compliant. Its self-serve plans cover ChatGPT, Claude, Gemini, Perplexity, Google AI Mode and AI Overviews, and Meta AI, with 350 to 700 custom prompts; SAML/OIDC SSO and the Data API are reserved for Enterprise. Enterprise brands with complex AEO programs, multiple markets, and C-suite reporting requirements will find Profound significantly better equipped.
What's the difference between Profound's prompt volumes and Scrunch's AI Search Trends?
Scrunch's AI Search Trends estimates AI search volume at the topic level using third-party data. By Scrunch's own description, this data is "directional, not exact"; teams can see which themes are trending, but not which specific prompts carry real demand. Profound's Prompt Volumes draws from 2B+ real user conversations with answer engines, broken down by intent, age, income, country, and platform. Teams can see which specific prompts carry the most volume and use that data to prioritize content decisions.
How does Profound's front-end querying differ from Scrunch's data collection?
Scrunch uses a combination of browser automation and official platform APIs to collect AI responses. Profound runs all prompts through the front-end browser, not through API calls. API responses and front-end responses from the same LLM can differ since the front-end reflects the full model behavior that real users encounter. Front-end querying produces visibility data that mirrors exactly what your customers see when they ask AI engines a question about your category. That's the experience you're trying to influence, so it's the one worth measuring.