Peec AI vs Scrunch AI vs Developer Marketing Hub: Which One Moves AI Rankings?

TLDR

• 73% of B2B buyers now use AI tools in their research process. If your brand is not showing up in those answers, you are already losing deals you never knew were in play.
• Peec AI tells you your GEO score across ChatGPT, Perplexity, and Gemini. Clean visibility data, solid competitor benchmarking, zero execution.
• Scrunch AI adds a technical layer on top of monitoring: hallucination detection, AI shopping visibility, and an agent experience platform that makes your existing pages more parseable to LLMs. Still no content, still no citation building.
• Developer Marketing Hub by Infrasity maps your exact buyer prompt clusters, identifies which content formats each LLM is pulling, surfaces the specific Reddit threads and URLs being cited instead of you, and builds everything required to get you into those answers.

Overview of Peec AI vs Scrunch AI vs Developer Marketing Hub

73% of B2B buyers now use AI tools like ChatGPT and Perplexity in their research process, and only 22% of marketers currently track AI visibility. That gap is where your pipeline is leaking.

The entire category of tools being compared in this article, Peec AI, Scrunch AI, and Developer Marketing Hub by Infrasity, exists to close it. The problem is they close it in very different ways, and picking the wrong one means you keep paying for a dashboard that tells you how bad the problem is without ever actually fixing it.

This blog will be covering how each tool works in practice, where each one wins, where each one breaks, and which one belongs in your stack depending on the stage your team is at.

Peec AI is a tracking platform. It tells you where your brand appears across ChatGPT, Perplexity, and Gemini, benchmarks you against competitors, and gives your team clean visibility data to work with. What it does not do is improve your position. You get the score, not the coaching.

Scrunch AI sits one layer deeper. It adds brand-sentiment context, prompt management, citation tracking, and page-level readiness audits on top of monitoring. It is built for teams that want to understand not just whether they are visible, but also how they are perceived in AI conversations. The problem and fall short with Scrunch AI is the same: insights land in your dashboard, execution stays on your plate.

Developer Marketing Hub by Infrasity is the only one in this comparison that closes the loop. It tracks AI visibility across major LLMs the same way the other two do, but it also builds content, structures documentation, runs Reddit positioning, and actively optimizes for citations. The difference is not a feature.

This blog will be covering how each tool works in practice, where each one wins, where each one breaks, and which one belongs in your stack depending on the stage your team is at.

1. Infrasity’s Developer Marketing Hub

There is a specific niche of companies that Peec AI and Scrunch AI were not built for: a B2B SaaS team whose buyers are developers and whose sales motion depends entirely on whether the technical community trusts them enough to recommend, cite, and evaluate their product.

Developer Marketing Hub by Infrasity was built for exactly that company. Not as a dashboard to track the problem, but as an execution infrastructure to fix it.

Infrasity has been working with 50+ B2B startups and scaleups, including Lovable, Qodo, Firefly, Kubiya, Vapi, Ox Security, Lightrun, Aviator, and Middleware, almost all of them developer-first or developer-led products. The results that come out of that client base are what separate it most clearly from the other two tools in this comparison:

Qodo now ranks first in ChatGPT for "Best AI Code Review Platform." Lovable gets cited in Claude for AI app development workflows, Firefly drove 781% organic traffic growth and 27% signup growth within three months of a single sprint, and Lightrun 3x'd inbound traffic with a 7% signup lift inside 30 days.

How Does Developer Marketing Hub by Infrasity Improve Your Brand's LLM Citation Visibility?

The product is built around four execution pillars that work together as a compounding system rather than as standalone services.

How Developer Marketing Hub Maps Prompt Clusters, Content Gaps, and LLM Citation Sources

a) Prompt Cluster Mapping

Everything in Developer Marketing Hub starts with prompt clusters. Before any content is created, any Reddit thread is engaged, or any documentation is restructured, DMH maps the exact set of prompts your buyers are running inside AI models right now. Not generic category queries.

The specific questions a developer asks when they are six hours into evaluating tools and about to make a shortlist decision. "Best alternative to X," "Y vs Z for enterprise workflows," "how to implement A without breaking B." That cluster becomes the baseline against which everything else is measured.

Once the cluster is mapped, DMH surfaces three things simultaneously: your current visibility score for each prompt, your competitors' presence on the same prompts, and a model-by-model breakdown showing how ChatGPT, Perplexity, Gemini, and Claude respond differently to each query.

The same brand appears in Perplexity but not in ChatGPT. A competitor dominates Claude but is absent from Gemini. That gap is not random; it is structural, and DMH tells you exactly where it is before anyone starts doing any work.

b) Content Categorization: Which Type of Content LLMs Are Actually Picking

This is the feature that most tools in this category lack and the one that makes the biggest practical difference. Everyone who has looked at GEO knows the answer is "create more content." DMH tells you what kind of content to create for each specific prompt cluster, because the content format that LLMs pull for "best AI code review tools" is structurally different from what they pull for "how to set up automated PR reviews in five steps."

For each prompt cluster, DMH identifies whether the AI models are preferencing how-to guides, listicles, comparative breakdowns, use-case walkthroughs, or opinion-led editorial pieces.

For example, for the Developer Marketing Prompt cluster, Listicle type of content performs at the best. Developer Marketing Platform

c) Content Opportunity

On top of format categorization, DMH generates actual topic recommendations tied directly to each prompt cluster. Not a generic content calendar. Specific titles, angles, and structures that fill the citation gaps identified in the prompt cluster mapping.

If the data shows your brand is invisible on "best developer marketing agency for YC startups" and the LLMs are pulling comparative listicles for that query, the output is a specific brief: here is the format, here is the angle, here is what the top-cited piece in this slot currently looks like, and here is how to displace it.

d) Model-Specific UI Output

The platform renders results in the same visual format as the actual models. When you are looking at how ChatGPT responds to a prompt in your cluster, it looks like ChatGPT. When you are looking at Perplexity, it looks like Perplexity.

It removes the abstraction layer between the data and reality. Your team sees exactly what your buyer sees when they ask that question, which sources are cited, where your brand sits in the response, and what would need to change for it to move up or appear at all.

What Developer Marketing Hub by Infrasity Does Not Do and Who It Is Not Built For

The platform surfaces which content to create, which format to use, and which gaps to close. The actual content creation is handled by Infrasity's in-house team, not generated automatically by the platform. That means the output is dependent on the team behind it, and the execution timeline is tied to human capacity rather than software speed.

DMH is also built entirely around B2B SaaS with developer and technical buyer personas. If you are a DTC brand, a retail company, or a consumer product company, the platform has no shopping-visibility layer. Scrunch AI tracks how specific SKUs appear in ChatGPT's shopping interface and identifies which retailers are driving those placements. DMH does not have that capability. For any brand where product-level AI shopping visibility matters, Scrunch is the more relevant tool.

2. What Is Peec AI and What Does It Actually Do?

Peec AI is an AI search analytics platform built for marketing teams that want to track how their brand performs across AI-powered search engines. Peec AI has raised $29 million in total: a €7 million seed round led by 20VC in July 2025, followed by a $21 million Series A led by Singular in November 2025, with participation from Antler, Combination VC, identity.vc, and S20, one of the largest early-stage rounds in the AI search category to date.

It monitors your visibility across ChatGPT, Perplexity, Google AI Overviews, and Google AI Mode as standard, with Claude, Gemini, Grok, Microsoft Copilot, and DeepSeek available as paid add-ons, benchmarks your position against competitors, and surfaces that data in a clean dashboard your team can log into without a manual.

What Features Does Peec AI Offer for GEO and LLM Visibility Tracking?

Peec AI's core feature set revolves around four things: tracking your brand's visibility across AI platforms, measuring how you rank and how you are perceived, benchmarking that performance against competitors, and surfacing the citation sources that are driving or blocking your presence. Everything in the product feeds into those four functions. Here is how each one works.

a) Prompt setup and management

Prompts are the foundation of the Peec AI workflow. The platform suggests prompts based on domain mapping and lets you organize them with tags, within the platform, like branded, non-branded, informational, and track them across countries, and filter by funnel stage or persona.

You can build out a library of queries that mirror how your actual buyers evaluate tools in your category, run them across models, and compare how your brand performs against competitors on each one.

b) Source tracking: used vs cited

This is one of Peec AI's more granular features. The platform tracks both "used" citations, where your content informed the AI answer without being named, and "cited" citations, where your URL is explicitly mentioned.

You can view source usage at the domain or URL level, complete with citation frequency. Knowing which of your pages are being pulled into AI responses, even when not credited, gives your content team a clearer picture of what is performing and what is not.

c) Segmentation and model coverage

You can filter results by model, country IP, and prompt tags, which lets you compare performance across segments such as US versus UK or awareness versus purchase intent prompts. This is useful for teams operating across markets or testing whether messaging lands differently across ChatGPT, Perplexity, and Gemini.

d) Reporting infrastructure

Peec AI supports CSV exports, a Looker Studio community connector for custom client-ready dashboards, and an API for teams that want to pull visibility data into existing reporting stacks. There is also an MCP integration that connects Peec to Claude, Cursor, n8n, and other tools in your stack for custom workflow automation.

e) The Act on Insights feature and where it stops

Peec AI surfaces a recommendations layer inside the dashboard. In practice, these read as: "G2 is regularly cited, make sure you have a profile with reviews," "Reddit discussions show up frequently in sources, consider joining the conversation," "Editorial domains are often cited, consider investing in digital PR."

No content gets written, no Reddit threads get engaged, no documentation gets restructured. The gap between the insight and the outcome is entirely on your team to close.

What Does Peec AI Not Do? Limitations That Matter Before You Commit

The Act on Insights feature in the dashboard surfaces recommendations such as "G2 is regularly cited, get a profile," "Reddit discussions show up in sources, consider joining the conversation," and "editorial domains are cited, consider investing in digital PR." Every one of those observations is accurate. None of them gets executed by the platform. The gap between the insight and the outcome lies entirely with your team.

There is no content creation. No documentation restructuring. No Reddit engagement. No citation building. No GEO optimization. Peec AI identifies the channels your brand needs to show up in and stops there. If your team has the writers, strategists, and community managers to act on that signal, the data is genuinely useful. If your team does not have that capacity, you are paying for a progressively clearer picture of a problem that is not getting any smaller.

3. Scrunch AI: Features, Metrics, and Where It Stops

Scrunch is trusted by 500+ companies and agencies, including Lenovo, Skims, Crunchbase, and Penn State. It positions itself as an AI customer experience platform, which is a wider brief than pure tracking.

Where Peec AI is built around measuring your visibility score, Scrunch is built around understanding the full picture of how AI models interact with your brand and your website.

Scrunch's product is organized across three pillars: Monitoring, Insights, and the Agent Experience Platform. Each one does a different job. Here is how they actually work.

What Features Does Scrunch AI Offer for AI Brand Monitoring and GEO Intelligence?

Scrunch covers more ground than most tools in this category. It tracks brand presence across AI platforms, maps the citation sources that shape your visibility, monitors how AI bots crawl your site in real time, attributes human traffic arriving from AI engines, and automatically generates a parallel AI-optimized version of your website.

a) Prompt Monitoring

Scrunch tracks how often your brand is included in AI answers and where it is placed in the response, with filters by topic, persona, model, and region. Coverage spans ChatGPT, Claude, Gemini, Perplexity, Grok, and Google AI Overviews, which is broader than Peec AI's current engine coverage.

The competitive benchmarking layer lets you compare the share of answers against named competitors across specific prompt sets, giving you a prompt-by-prompt view of where you are winning and where you are getting displaced.

b) Actionable Insights

The Insights layer surfaces which sources and pages AI uses to build its answers, runs automatic error detection to catch technical issues that prevent AI agents from consuming your content, and includes a page optimizer that identifies content gaps that are reducing your brand presence in AI answers.

The content recommendations go one level deeper than Peec AI's surface-level suggestions. Rather than "consider joining Reddit," Scrunch surfaces specific page-level gaps: missing product specs, unclear pricing structures, entities that AI cannot resolve, and claims that lack supporting sources

c) The Agent Experience Platform (AXP)

AXP is Scrunch's most distinctive technical feature and the one that most clearly separates it from Peec AI. AXP creates a parallel, AI-ready version of your pages that is automatically served to AI agents at the CDN layer, with no code changes required and no impact on the human-facing site.

It intercepts AI retrieval traffic, translates pages into clean server-rendered HTML without JavaScript dependencies, adds structured summaries, and simplifies code so agents can parse and cite your content accurately

d) AI shopping visibility

Scrunch has e-commerce features that let you track how specific SKUs appear in ChatGPT's shopping interface and identify which retailers are driving those placements. This is a meaningful addition for D2C and retail brands because it moves the tracking from brand-level mentions down to product-level recommendations.

What Does Scrunch AI Not Do? Where It Stops Short of Full GEO Execution

AXP is genuinely useful technical infrastructure. The catch is that it optimizes what you already have. If your existing content is thin, poorly structured, or does not address the prompts your buyers are actually using, AXP makes it more readable to AI models. It does not make it better.

It does not write new content, build out topic coverage, position your brand in third-party communities, or create the citation signals that AI models use to decide whether a brand is worth recommending in the first place.

The same is true of the content recommendations inside Insights. Scrunch will tell you that your pricing page is missing structured definitions, that your product comparison content lacks specificity, and that your category landing page has entity gaps.

Scrunch is a tool for teams that already have content capacity and need a more sophisticated monitoring and technical layer to direct it. For teams without that capacity, it adds diagnostic depth to the same fundamental problem that Peec AI surfaces: you know exactly what is wrong, and you still have no mechanism to fix it.

Head-on Comparison: Peec AI v/s Scrunch AI v/s DMH

AI search has fundamentally changed how B2B buyers discover, evaluate, and shortlist products, and having no visibility into that process is no longer defensible for any growth-stage company.

Conclusion: Which Tool Is Actually Right for You

If your team already has writers, strategists, and community managers in place and you need a data layer to tell them where to focus, Peec AI gives you clean, reliable visibility data across the AI engines that matter without unnecessary complexity.

If you are a mid-market or enterprise brand with a technical content team in-house, or if you are in D2C or retail, where product-level AI shopping visibility and hallucination detection are genuine concerns, Scrunch AI is the more complete monitoring platform.

If your buyers are developers or technical decision-makers, your team does not have the internal capacity to build GEO infrastructure from scratch, and you need a partner that maps your prompt clusters, identifies exactly what is keeping you off the AI shortlist, and builds the content, documentation, and community presence required to change that, Developer Marketing Hub is the only option in this comparison that closes the loop.

Peec AI and Scrunch AI tell you where the gap is. DMH closes it

Frequently Asked Questions

1. How do I get my brand cited in ChatGPT and Perplexity as a B2B SaaS company?

LLMs consistently pull from G2, Capterra, comparison pages, and Reddit threads rather than brand websites. Getting cited means building presence in the third-party sources AI models already trust: structured comparison content, community discussions in relevant subreddits, and authoritative external publications. 89% of cited links in AI responses are earned media, not owned content. If your strategy is limited to optimizing your own website, you are building for the wrong signal.

2. What is the difference between GEO and SEO and why does it matter for developer tools?

SEO optimizes your content to rank in Google search results through keyword signals and backlinks. GEO, generative engine optimization, optimizes your content, documentation, and third-party brand signals to get cited in AI-generated answers across ChatGPT, Perplexity, Claude, and Gemini. Research published in 2026 found that 60% of sources cited by AI tools are not even in Google's top 10 organic results.

3. Which AI visibility tool is best specifically for developer marketing and technical buyer personas?

Developer Marketing Hub by Infrasity is the only tool in this category built specifically for B2B SaaS companies serving developers and technical decision-makers. Peec AI and Scrunch AI are horizontal monitoring platforms that operate across verticals but lack a developer-specific content execution layer, a technical community positioning capability, and an understanding of how developers evaluate tools differently from traditional B2B buyers.

4. What is prompt cluster mapping, and why does it matter for GEO?

Prompt cluster mapping is the process of identifying the specific set of queries your buyers are running inside AI models at different stages of the evaluation process. Not generic category keywords, but the exact prompts a developer uses when they are six hours into comparing tools and about to make a shortlist decision.

It matters for GEO because optimizing for AI search as a monolithic category is like optimizing for social media without distinguishing between LinkedIn and TikTok. Different prompts pull different content formats from different sources.