HubSpot, Semrush, Adobe, and Conductor Enter GEO — How Incumbents Are Moving

Background: Big Players Are Entering the GEO Market

Starting in H2 2025, major players in the marketing and SEO industry began launching dedicated GEO (Generative Engine Optimization) products and content in earnest. What had been a space defined by academic research (Aggarwal et al., KDD 2024) and early-stage startups (Profound, Scrunch, Peec, etc.) now had incumbents with established infrastructure and customer bases numbering in the millions.

This post analyzes the GEO-related moves of four companies — HubSpot, Semrush, Adobe, and Conductor — across their features, pricing, strategies, and limitations. It also examines Pinterest’s production-scale application and draws implications for the broader GEO market structure.

The landscape of early-stage GEO startups is covered separately in “GEO SaaS Landscape Overview.” This post focuses specifically on incumbent entry strategies.

Why Now

The timing of incumbent entry is no coincidence. Three conditions aligned simultaneously in H2 2025:

1. AI search traffic reached a critical threshold: According to Conductor’s 2026 AEO/GEO Benchmarks Report, AI responses appear in roughly one-quarter of Google searches, approaching 50% in verticals like healthcare and finance.
2. Customer demand became visible: Existing SEO tool customers began asking, “How does our brand appear in AI search results?”
3. Competitive necessity: GEO-native startups (Profound $23.5M, Bluefish $24M) secured funding and started winning enterprise clients, making customer attrition a real possibility for incumbents.

timeline
    title Big Player GEO Entry Timeline
    2024 H2 : Aggarwal et al. KDD 2024 paper published
             : Academic definition of GEO established
    2025 H1 : GEO startup seed/Series A investment wave
             : Profound, Scrunch, Peec launch products
    2025 Q3 : HubSpot AI Search Grader launch
             : Semrush AI Visibility Toolkit launch
    2025 Q4 : Adobe LLM Optimizer GA
             : Conductor AEO/GEO Benchmarks Report published
    2026 Q1 : Semrush One integrated bundle launch
             : Big players iterate on GEO features

The critical observation from this timeline is that the progression from academic definition (2024 H2) to startup investment (2025 H1) to incumbent entry (2025 H2) was compressed into just 12 months. In SEO, the equivalent progression took 5-7 years. The GEO market is forming far faster than SEO did, because the underlying infrastructure — AI APIs, cloud computing, data pipelines — is already mature.

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Detailed Analysis by Player

HubSpot: Free Diagnostic Tools for Market Education

Product Overview

HubSpot launched two free tools:

Tool	Launch	AI Engines Analyzed	Price
AI Search Grader	H2 2025	GPT-4o based	Free
AEO Grader	H2 2025	ChatGPT, Perplexity, Gemini	Free

Despite different names, the core functionality is similar. Users enter a brand name or URL, and the tool diagnoses how that brand is mentioned across major AI search engines.

How It Works

The AEO Grader analysis process:

1. User enters a brand name, URL, or industry keyword
2. The tool automatically generates dozens of test queries
3. Queries are run against ChatGPT, Perplexity, and Gemini
4. Brand mention frequency, position, and context are analyzed from AI responses
5. A comprehensive report is generated within 3-5 minutes

The report includes four core metrics:

Metric	Description
Overall Grade	Composite score across all AI search engines
Brand Sentiment Score	Sentiment analysis of how AI responses describe the brand
Share of Voice Score	Brand mention share relative to competitors
Market Position	Classification as Leader / Challenger / Niche Player

Strategic Intent

HubSpot’s approach is a textbook Free Tool > Lead Capture > Paid Conversion playbook. HubSpot has a long history of using free diagnostic tools — Website Grader, Marketing Grader — to capture the top of the marketing funnel. AI Search Grader is the GEO iteration of this pattern.

HubSpot is not monetizing GEO directly. The goal is to educate the market on a new category while securing the top of the inbound funnel for its existing marketing platform.

The impact on the GEO market is twofold:

Market education effect (positive): HubSpot’s marketing reach is among the strongest in the industry. The HubSpot blog draws tens of millions of monthly visitors, and HubSpot Academy has certified millions. When the concept of “AI search visibility” is disseminated through these channels, overall GEO market awareness rises. The time and cost startups spend explaining “what GEO is” drops dramatically.

Baseline demand absorption (negative): If a free tool satisfies the basic desire to understand “how does our brand look in AI search,” paid GEO tools face a harder entry. The objection becomes, “Why pay when I can get this for free?”

Limitations and Gaps

As a free tool, functional limitations are clear:

Limitation	Detail
No keyword customization	Cannot scope Share of Voice reports to specific keyword sets
No manual competitor selection	Relies on auto-generated competitor lists, which may include irrelevant brands
Simplified sentiment analysis	”Neutral” verdicts may miss subtle positive/negative nuances
One-time snapshot	No continuous monitoring. No time-series tracking
Cross-engine inconsistency	Same query produces different responses across ChatGPT, Perplexity, and Gemini, but the reconciliation logic is opaque
English only	No multilingual analysis

These limitations are partly intentional. The free tool’s role is market education and lead generation, not replacing dedicated GEO solutions. These gaps represent precisely the differentiation territory that specialized GEO tools can exploit.

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Semrush: Adding an AI Visibility Layer to Existing SEO Tools

Product Overview

Semrush launched the AI Visibility Toolkit as a standalone product, then folded it into the Semrush One integrated bundle in October 2025. The strategy: layer GEO capabilities on top of a platform already used by millions of SEO practitioners.

Core Features

Feature	Description
AI Visibility Score	Scores brand exposure frequency across ChatGPT, Perplexity, Gemini, and Google AI Overviews
Prompt Monitoring	Tracks questions users ask AI platforms, segmented by industry, competitor, and brand. Monitors millions of prompts daily
Overall Sentiment	Sentiment analysis of how AI platforms describe the brand
Topic Opportunity	Identifies queries where competitors are mentioned but your brand is not

The key differentiator from HubSpot is continuous monitoring. While HubSpot provides a one-time snapshot, Semrush accumulates time-series data and tracks changes. This difference is decisive. AI search engine responses shift constantly due to model updates, training data changes, and real-time information integration. A brand recommended as #1 yesterday can vanish today. Without continuous monitoring, these changes go undetected.

Prompt Monitoring is a uniquely powerful feature. It collects and analyzes at scale what users actually ask AI engines. Think of it as the GEO equivalent of keyword research. Just as SEO required knowing “what people search on Google,” GEO requires knowing “what people ask AI.”

Topic Opportunity is equally noteworthy. It automatically identifies queries where competitors appear in AI responses but your brand does not. This directly prioritizes GEO strategy execution.

Pricing Structure

Plan	Monthly Price	Coverage
AI Visibility Toolkit (standalone)	$99/mo	AI visibility tracking, $99/mo per additional domain
Semrush One Starter	$199/mo	SEO + AI Visibility integrated
Semrush One Pro+	$299/mo	Extended features
Semrush One Advanced	$549/mo	Full feature set

Semrush One saves approximately $40/month compared to purchasing SEO tools and the AI Visibility Toolkit separately. Bundle discounts are a standard strategy for locking customers into an integrated platform.

The $99/month standalone price creates direct pricing pressure on specialized GEO startups. Considering Semrush’s brand recognition, data assets, and existing user base, competing at the same price point is extremely difficult for specialized tools. They must differentiate on depth that Semrush cannot match.

Strategic Positioning

Semrush’s strategy is to embed GEO into existing workflows. When AI visibility tabs appear in the Semrush dashboard that SEO practitioners already open daily, the incentive to adopt a separate GEO-specific tool diminishes.

flowchart LR
    A[Existing SEO Dashboard] --> B[Keyword Tracking]
    A --> C[Backlink Analysis]
    A --> D[Site Audit]
    A --> E[AI Visibility Toolkit]
    E --> F[AI Visibility Score]
    E --> G[Prompt Monitoring]
    E --> H[Sentiment Analysis]
    E --> I[Topic Opportunity]
    style E fill:#f9f,stroke:#333,stroke-width:2px

The existing customer base and data assets provide low switching costs. However, there is a structural limitation to this approach. When GEO features are positioned as “add-ons” to an existing SEO tool, they may be deprioritized on the product roadmap relative to core SEO features. The majority of Semrush’s revenue still comes from SEO, which limits the development resources that can be allocated to GEO.

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Adobe: Integrating GEO Into the Enterprise Content Pipeline

Product Overview

Adobe launched LLM Optimizer to GA (General Availability) in October 2025. The tool analyzes how brand content appears in large language model responses and supports optimization to increase citation probability.

Core Features

Feature Area	Description
Brand Visibility Measurement	Tracks brand exposure frequency, position, and context in AI search results; compares Share of Voice against competitors
Content Optimization Engine	Detects brand visibility gaps and recommends improvements across both owned channels (web pages, FAQs) and external channels (Wikipedia, public forums)
Business Impact Attribution	Connects AI visibility to user behavior (engagement, conversions)
Enterprise Integration	Native integration with Adobe Experience Manager Sites. Supports A2A (Agent-to-Agent) and MCP (Model Context Protocol) standards

What fundamentally distinguishes Adobe from the other three players is the Content Optimization Engine. While other tools stop at “here is your AI visibility,” Adobe provides specific recommendations like “modify this content to improve AI visibility” — and lets you execute those changes directly in AEM Sites. It closes the gap between analysis and action.

Business Impact Attribution is also critical for enterprise customers. “AI visibility went up” alone is insufficient to secure C-level budget approval. The business case requires connecting the dots: “AI visibility improvement drove a X% increase in website conversion rates.” Native integration with Adobe Analytics makes this possible.

Pricing Structure

Adobe LLM Optimizer uses prompt-based pricing.

Item	Detail
Minimum purchase	1,000 prompts
Starting price	$115,000/year (for 1,000 prompts)
Additional purchases	200-prompt increments
Volume discount	Yes (per-prompt cost decreases with volume)
Free trial	200 prompts free for AEM Cloud customers

The $115,000/year starting price makes it unmistakably clear this tool targets the enterprise market. SMBs and individual marketers are not the audience.

Prompt-based pricing scales with usage, so costs increase for companies managing large brand portfolios. Volume discounts offset this somewhat, lowering per-prompt costs as consumption grows.

Strategic Positioning

Adobe’s approach is fundamentally different from the other three players. While HubSpot educates the market with free tools and Semrush bundles GEO into existing SEO products, Adobe positions GEO as a natural extension of the enterprise content pipeline.

flowchart TB
    subgraph Adobe Experience Cloud
        AEM[AEM Sites<br>Content Management]
        AA[Adobe Analytics<br>Analytics]
        AT[Adobe Target<br>Personalization]
    end
    subgraph LLM Optimizer
        VM[Visibility<br>Measurement]
        CO[Content<br>Optimization]
        BA[Business Impact<br>Attribution]
    end
    AEM --> VM
    VM --> CO
    CO --> AEM
    BA --> AA
    style LLM Optimizer fill:#e6f3ff,stroke:#333

A2A and MCP standard support is worth highlighting. Adobe supporting agent-to-agent communication protocols signals that it is designing GEO not as a simple analytics tool but as a node in the AI agent ecosystem. The envisioned future: when an AI agent requests “tell me about this brand,” Adobe’s optimized content is delivered in a structured format.

What makes Adobe’s approach particularly threatening is that it resolves GEO at the content creation stage. Other GEO tools take a reactive approach — “analyze the AI visibility of already-published content.” Adobe takes a proactive approach — “optimize content for LLMs before it is published.” This difference is fundamental.

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Conductor: Research and Education Content for Enterprise Positioning

Approach

Unlike the other three, Conductor has not launched a standalone GEO product. Instead, it is building a thought leadership position through research reports and educational content.

Key Publications

Publication	Date	Core Content
2026 AEO/GEO Benchmarks Report	Nov 2025	Analysis of 13,770 domains, 21.9M Google searches, 17M AI responses, 100M AI citations
State of AEO/GEO in 2026: CMO Investment Report	Jan 2026	Enterprise CMO survey on AEO/GEO investment
Enterprise AEO Guide	H2 2025	GEO adoption guide for enterprise decision-makers

Benchmarks Report Key Data

Conductor’s 2026 AEO/GEO Benchmarks Report is based on the largest publicly available GEO research dataset.

Metric	Value
Sessions analyzed	3.3 billion
Domains analyzed	13,770
Google searches analyzed	21.9 million
AI-generated responses analyzed	17 million
AI citations analyzed	100 million
AI response exposure share	~25% of Google searches (up to ~50% in some industries)
ChatGPT share of AI referral traffic	~90%

Conductor’s data showing AI referral traffic at only ~1% of total traffic provides a fact-based correction to the overblown narrative that “AI search is replacing traditional search.” AI is redefining how discovery begins, not replacing organic search itself.

The market impact of this data is substantial. It transforms the vague belief that “AI search matters” into concrete numbers. When marketing teams request GEO budgets from the C-suite, having data like “AI responses appear in 25% of Google searches, approaching 50% in healthcare and finance” is a compelling argument.

Strategic Intent

Conductor’s research leadership strategy is a long game. The goal is to ensure that when enterprise decision-makers move from “What is GEO?” to “We need GEO — which tool should we use?”, Conductor is the first name that comes to mind. Conductor was already named a leader in AI-integrated SEO by Forrester Wave 2025, and integrating GEO features into its existing enterprise SEO platform is a matter of time.

If Conductor’s benchmark report becomes the industry’s standard reference, the company that created that standard holds a powerful positioning advantage. This is analogous to how Gartner’s “Magic Quadrant” defines markets — and then sells consulting services within those definitions.

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Comparing Entry Strategy Patterns

Comparing the four companies’ entry strategies reveals clear patterns.

Strategy Type Classification

quadrantChart
    title Big Player GEO Entry Strategy Matrix
    x-axis "Standalone Product" --> "Bundle/Integration"
    y-axis "SMB/Mid-Market" --> "Enterprise"
    quadrant-1 "Enterprise Bundle"
    quadrant-2 "Enterprise Standalone"
    quadrant-3 "SMB Standalone"
    quadrant-4 "SMB Bundle"
    "Adobe LLM Optimizer": [0.8, 0.9]
    "Semrush AI Visibility": [0.7, 0.4]
    "HubSpot AEO Grader": [0.3, 0.3]
    "Conductor": [0.6, 0.85]

Comprehensive Comparison

Dimension	HubSpot	Semrush	Adobe	Conductor
Product form	Free diagnostic tool	Paid standalone module + bundle	Enterprise standalone product	Research/education content + existing platform
Entry strategy	Free Tool > Lead Capture	Bundle Upsell	Enterprise Upsell	Thought Leadership > Platform Upsell
Target customer	SMB marketers	SEO practitioners (SMB to Mid)	Enterprise marketing/content teams	Enterprise CMOs/VPs
Pricing	Free	$99-$549/mo	$115,000+/yr	Custom (undisclosed)
AI engines analyzed	ChatGPT, Perplexity, Gemini	ChatGPT, Perplexity, Gemini, AI Overviews	LLMs broadly	Google AI Overviews, ChatGPT, etc.
Monitoring type	One-time snapshot	Continuous monitoring	Continuous monitoring	Benchmark reports (periodic)
Existing platform integration	HubSpot CRM/Marketing Hub	Semrush SEO Suite	Adobe Experience Cloud	Conductor Intelligence
GEO monetization	Indirect (lead capture)	Direct (subscription)	Direct (prompt-based)	Indirect (included in platform subscription)

Feature Coverage Comparison

A detailed breakdown of which GEO capabilities each tool actually covers:

Feature	HubSpot	Semrush	Adobe	Conductor
AI visibility score	O	O	O	O (report)
Brand sentiment analysis	O	O	O	-
Competitor benchmarking	Partial (auto-selected)	O	O	O (report)
Per-keyword tracking	X	O	O	Partial
Time-series monitoring	X	O	O	X (periodic reports)
Prompt monitoring	X	O	-	-
Content optimization recommendations	X	Partial	O	Partial (guide)
Attribution	X	Partial	O	X
Enterprise governance	X	X	O	O
API access	X	O	O	O
Multilingual support	X (English only)	Partial	O	Partial
Automated workflows	X	X	Partial	X

(O = supported, Partial = limited support, X = not supported, - = unconfirmed)

All four companies show gaps in real-time AI response change detection, cross-engine response inconsistency analysis, and automated content modification execution. These three areas represent the clearest differentiation points for specialized GEO startups.

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Historical Patterns: Free-to-Paid Transitions

The history of the SEO market offers a useful reference for predicting the GEO market’s future.

graph LR
    subgraph SEOMarket["SEO Market (2005-2015)"]
        GA[Google Analytics<br/>Free] --> |Market education| SEOToolGrowth[Ahrefs/Moz/SEMrush<br/>Paid growth]
        GSC[Google Search Console<br/>Free] --> |Baseline demand| SEOToolGrowth
        SEOToolGrowth --> |Premium features| SEOMature[SEO tool market maturity<br/>$10B+]
    end

    subgraph GEOMarket["GEO Market (2025-?)"]
        HG[HubSpot Grader<br/>Free] --> |Market education| GEOToolGrowth[Specialized GEO tools<br/>Paid growth?]
        SV[Semrush AI Visibility<br/>Bundle] --> |Baseline demand| GEOToolGrowth
        GEOToolGrowth --> |Premium features| GEOMature[GEO tool market maturity?]
    end

The Free-Paid Coexistence Pattern in SEO

Between 2005 and 2010, Google offered Google Analytics and Search Console for free. These free tools served three functions:

1. Market education: They instilled the belief that “analyzing website traffic matters” among millions of webmasters
2. Baseline demand fulfillment: Simple traffic analysis and search ranking checks were possible with free tools
3. Advanced demand creation: Users who tried basic analysis with free tools wanted “deeper analysis,” generating demand for paid tools

The result: Google’s free tools did not kill the market. They grew the total market, and paid tools like Ahrefs, Moz, and SEMrush grew independently. As of 2026, the SEO tool market exceeds $10B, with free and paid tools coexisting.

The Likely Parallel Pattern in GEO

A similar pattern is likely to repeat in GEO:

SEO Market	GEO Market (projected)
Google Analytics (free) > market education	HubSpot AI Search Grader (free) > market education
Google Search Console (free) > basic analysis	Semrush AI Visibility (bundle) > basic analysis
Ahrefs/Moz/SEMrush (paid) > advanced analysis	Profound/Peec/specialized tools (paid) > advanced analysis

There is, however, an important difference. In SEO, Google occupied the unique position of being both the search engine operator and the provider of free analytics tools. In GEO, the AI search engine operators (OpenAI, Google, Anthropic) have not yet released their own “AI visibility analytics tools.” Instead, marketing tool companies like HubSpot and Semrush are filling that role. If AI search engine operators begin offering GEO tools directly, the market structure could be reshaped once again.

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Buy vs. Build: How Incumbents Acquired GEO Capabilities

When entering the GEO market, a critical decision for incumbents is Build vs. Buy vs. Partner.

Each Company’s Choice

Company	Choice	Rationale
HubSpot	Build	Free diagnostic tool scope keeps development costs low. Core requirement is AI API calls + response parsing logic
Semrush	Build	Existing crawling/data pipeline infrastructure can be leveraged. AI search monitoring is an extension of existing SERP monitoring
Adobe	Build + Integrate	In-house development + deep integration with Adobe Experience Cloud. Native AEM Sites integration would be difficult via external acquisition
Conductor	Build (research)	Currently investing in research over products. Building benchmarks from large-scale datasets

All four chose to build internally rather than acquire major GEO startups. This reflects the fact that core GEO technology barriers are not yet high, and deep integration with existing platforms is the key value driver.

However, as the market matures and specialized startups accumulate proprietary datasets and algorithms, acquisition strategies may emerge. Companies like Profound ($23.5M funding, NVIDIA-backed) and Bluefish ($24M funding, retail-specialized) are potential acquisition targets.

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Industry Application Case: Pinterest

Paper Overview

Zhang et al. (Pinterest, 2026) published a VLM (Vision-Language Model)-based agentic trend mining framework applied to large-scale collection pages (arXiv:2602.02961).

This case is significant because it is one of the first publicly documented applications of GEO in a large-scale production system, rather than as a marketing vendor’s analytics feature.

Technical Architecture

Component	Role
VLM (Vision-Language Model)	Predicts search queries from images. Infers what users would actually search based on visual content
Agent Orchestration (DAG)	DAG-based agent execution flow with planning nodes and filtering nodes
Two-Tower ANN	Multimodal embedding-based semantic search connecting billions of images to tens of millions of collections
Authority-Aware Interlinking	Propagates authority signals across visual assets

flowchart TB
    subgraph "Pinterest GEO Framework"
        VLM[VLM<br>Image-to-Query Prediction]
        PLAN[Planning Node<br>Execution Strategy]
        FILTER[Filtering Node<br>Trend Relevance Classification]
        EMBED[Multimodal<br>Embedding]
        COLLECT[Collection Page<br>Generation/Optimization]
    end
    VLM --> PLAN
    PLAN --> FILTER
    FILTER --> EMBED
    EMBED --> COLLECT
    COLLECT -->|"20% organic traffic increase"| RESULT[Millions of incremental MAU]

Results

Metric	Value
Processing scale	Billions of images, tens of millions of collections
Organic traffic change	+20%
MAU impact	Contributed millions of incremental MAU

Implications

The Pinterest case carries significance on three dimensions that go beyond incumbent GEO tool releases:

1. Analysis vs. execution: While HubSpot/Semrush/Adobe focus on analyzing “how your brand appears in AI search,” Pinterest applied GEO directly to content generation and optimization automation. It demonstrates a future where AI doesn’t just analyze but directly optimizes content.
2. Production-scale validation: Achieving real MAU growth at a scale of billions of images proves that GEO can deliver production-level results beyond experimental concepts. A 20% organic traffic increase at Pinterest’s scale translates to tens of millions of additional sessions.
3. VLM + Agent architecture: This demonstrates that GEO can extend beyond text-based optimization into visual content. VLMs that simultaneously understand images and text open GEO possibilities for visually-driven platforms like Pinterest, Instagram, and YouTube.

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Market Implications

Multi-Layered Category Validation Signals

Incumbent entry is the strongest market signal that GEO is establishing itself as an independent product category, not a passing buzzword. The validation signals across multiple layers:

Validation Layer	Signal	Timing
Academic definition	Aggarwal et al. KDD 2024 — GEO concept formalized	2024
VC investment	Profound $23.5M, Bluefish $24M, Exa $85M	2025
Incumbent entry	HubSpot, Semrush, Adobe, Conductor	H2 2025
Production deployment	Pinterest — 20% organic traffic increase	2026
Analyst recognition	G2 AEO category created, Forrester Wave AI-SEO	2025-2026

All five layers of validation occurring within 12 months indicates that GEO is rapidly being legitimized as a market category.

Market Structure Forecast: Five-Layer Coexistence

Following incumbent entry, the GEO market is expected to differentiate into a layered structure:

Layer	Role	Projected Players	Price Range
Free/basic diagnostic	Market education, basic awareness	HubSpot Grader, free tools	$0
Bundle/integrated analytics	Baseline GEO within existing workflows	Semrush, Adobe, Ahrefs (projected)	$99-$549/mo
Specialized deep analytics	In-depth AI visibility analysis	Profound, Peec, AthenaHQ	$200-$2,000/mo
Infrastructure/data	AI response data infrastructure	Exa, Scrunch AI	API-based
Vertical-specific	Industry/region-specialized tools	Bluefish (retail), Asia-specific tools	Custom

Each layer serves different customer needs, enabling coexistence. The key for specialized startups is to differentiate clearly in the “specialized deep analytics” or “vertical-specific” layers without overlapping the “bundle/integrated analytics” layer.

The $100-$500/month range is currently occupied solely by Semrush, and this range is likely to become the primary battleground for GEO-native startups. Who captures the mid-market between free (HubSpot) and enterprise ($115K+, Adobe/Conductor) will determine the market’s future structure.

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References

• Zhang, F. et al. (Pinterest, 2026). Generative Engine Optimization: A VLM and Agent Framework for Pinterest Acquisition Growth. arXiv:2602.02961.
• Conductor (2025). The 2026 AEO/GEO Benchmarks Report.
• Conductor (2026). The State of AEO/GEO in 2026: CMO Investment Report.
• HubSpot (2025). From SEO to LMO: HubSpot launches the first free tool for AI discovery.
• Adobe (2025). Media Alert: Adobe Delivers LLM Optimizer for Businesses.
• Semrush (2025). AI Visibility Toolkit: Boost Brand Visibility in AI Search.