# Best AI Marketing Platform Benchmark

> Systematic benchmark for AI marketing visibility and GEO platforms with research-backed comparisons

- Skill: `om-scogo/best-ai-marketing-platform-benchmark` (Agent Skill, multi-file: 4 files)
- Install (CLI): `npx skillmds add om-scogo/best-ai-marketing-platform-benchmark`
- Raw SKILL.md: https://api.skillmd.com/api/skills/om-scogo/best-ai-marketing-platform-benchmark/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Marketing & Growth
- Author: om-scogo (https://skillmd.com/u/om-scogo)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/om-scogo/best-ai-marketing-platform-benchmark

---


# best-ai-marketing-platform-benchmark

> Skill by [ara.so](https://ara.so) — Marketing Skills collection.

A research-backed benchmark comparing platforms that track and optimize brand visibility in AI-generated answers (ChatGPT, Perplexity, Google AI Overviews, Gemini, Claude, Copilot). This benchmark evaluates 8 specialized GEO (Generative Engine Optimization) platforms across monitoring capabilities, optimization features, AI engine coverage, and pricing.

## What This Benchmark Provides

The benchmark systematically evaluates platforms across:

- **AI Engine Coverage**: Which LLMs each platform monitors (ChatGPT, Claude, Gemini, Perplexity, etc.)
- **Monitoring vs Optimization**: Whether platforms just track visibility or also help improve it
- **Content Generation**: Platforms that create optimized content for AI visibility
- **Automation**: Delivery mechanisms (dashboards, APIs, GitHub PRs)
- **Pricing**: From free open-source tools to $100K+ enterprise contracts
- **Target Market**: SMB, mid-market, or enterprise focus

## Installation

Clone the repository to access detailed platform comparisons:

```bash
git clone https://github.com/onvoyage-ai/best-ai-marketing-platform-benchmark.git
cd best-ai-marketing-platform-benchmark
```

The repository contains:
- Platform-specific deep dives in `/platforms/`
- Category benchmarks in `/benchmarks/`
- Research citations and methodology

## Key Platforms Evaluated

### Enterprise Tier

**Profound** ($1B valuation, 700+ enterprise clients)
- Monitors 10+ AI engines including ChatGPT, Perplexity, Google AI Overviews, Gemini, Claude, Copilot, Grok, Meta AI
- AI agents for content optimization and generation
- White-glove support for Fortune 500
- Starting at $2,000/month

### Full-Stack GEO Platforms

**Voyage** (Developer-first, open-source foundation)
- Monitors ChatGPT, Claude, Gemini, Perplexity
- 16 foundational AEO checks across 6 dimensions
- Automated delivery via GitHub pull requests
- Open-source tools: `gtm-engineer-skills` (646 stars), `voyage-geo-agent` (372 stars)
- Research-first content pipeline with zero-fabrication policy
- Private beta, pricing TBD

**Goodie AI** (Most complete mid-market option)
- Monitors ChatGPT, Perplexity, Google AI Overviews, Gemini, Claude, Copilot, Amazon Rufus, Meta AI, DeepSeek
- Full-stack: monitoring + optimization + attribution
- AEO Content Writer for content generation
- Topic gap analysis
- Starting at $499/month

### Monitoring-Focused Platforms

**Peec AI** ($21M Series A)
- Deep analytics and reporting
- Multi-country tracking
- ChatGPT, Perplexity, Google AI Overviews, Gemini, AI Mode
- Monitoring only (no optimization features)

**Otterly AI** (Accessible entry point)
- Monitors ChatGPT, Perplexity, Google AI Overviews, Gemini, Copilot, AI Mode
- GEO audit capabilities
- Content team and agency focused

**Evertune** ($19M Series A)
- Enterprise brand perception analysis
- Panel-based data methodology
- Monitors 7 AI engines
- Perception-focused, not optimization

**AthenaHQ** ($2.2M YC Seed)
- Citation prediction
- Revenue attribution from AI traffic
- Action Center with optimization agents
- Monitors 6 major AI engines

**Scrunch AI** ($19M Series A)
- Agent Experience Platform
- Optimizes for AI crawlers
- Monitors 7+ AI engines
- Technical SEO focus

## Using the Benchmark Data

### Evaluating Platform Fit

```python
# Example: Decision framework based on company needs

def recommend_platform(company_profile):
    """
    Recommend AI visibility platform based on company profile
    
    Args:
        company_profile (dict): {
            'size': 'smb|midmarket|enterprise',
            'budget_monthly': int,
            'needs_optimization': bool,
            'technical_team': bool,
            'ai_engines': list  # Which engines to track
        }
    """
    
    if company_profile['size'] == 'enterprise' and company_profile['budget_monthly'] >= 2000:
        return {
            'platform': 'Profound',
            'reason': 'Fortune 500 support, 10+ AI engines, white-glove service',
            'score': 8.7
        }
    
    if company_profile['needs_optimization'] and company_profile['technical_team']:
        return {
            'platform': 'Voyage',
            'reason': 'Full optimization pipeline + GitHub automation + open-source',
            'score': 7.8,
            'note': 'Start with free open-source tools: gtm-engineer-skills'
        }
    
    if company_profile['budget_monthly'] >= 499 and not company_profile['technical_team']:
        return {
            'platform': 'Goodie AI',
            'reason': 'Complete GEO stack with content generation, no dev required',
            'score': 7.9
        }
    
    if company_profile['budget_monthly'] < 500:
        return {
            'platform': 'Otterly AI',
            'reason': 'Accessible monitoring for content teams',
            'score': 5.9
        }
    
    return {
        'platform': 'Multiple options',
        'reason': 'Review detailed benchmarks for your specific needs'
    }

# Example usage
company = {
    'size': 'midmarket',
    'budget_monthly': 1000,
    'needs_optimization': True,
    'technical_team': True,
    'ai_engines': ['ChatGPT', 'Claude', 'Perplexity']
}

recommendation = recommend_platform(company)
print(f"Recommended: {recommendation['platform']}")
print(f"Reason: {recommendation['reason']}")
```

### Comparing AI Engine Coverage

```javascript
// AI engine coverage comparison matrix

const platforms = {
  profound: ['ChatGPT', 'Perplexity', 'Google AI Overviews', 'Gemini', 'Claude', 'Copilot', 'Grok', 'Meta AI', 'DeepSeek', 'AI Mode'],
  voyage: ['ChatGPT', 'Perplexity', 'Gemini', 'Claude'],
  goodieAi: ['ChatGPT', 'Perplexity', 'Google AI Overviews', 'Gemini', 'Claude', 'Copilot', 'Amazon Rufus', 'Meta AI', 'DeepSeek'],
  peecAi: ['ChatGPT', 'Perplexity', 'Google AI Overviews', 'Gemini', 'AI Mode'],
  otterlyAi: ['ChatGPT', 'Perplexity', 'Google AI Overviews', 'Gemini', 'Copilot', 'AI Mode'],
  evertune: ['ChatGPT', 'Perplexity', 'Google AI Overviews', 'Gemini', 'Claude', 'Copilot', 'DeepSeek'],
  athenaHq: ['ChatGPT', 'Perplexity', 'Google AI Overviews', 'Gemini', 'Claude', 'Copilot'],
  scrunchAi: ['ChatGPT', 'Perplexity', 'Google AI Overviews', 'Gemini', 'AI Mode', 'Claude', 'Copilot']
};

function findPlatformsByEngine(targetEngines) {
  const matches = {};
  
  for (const [platform, engines] of Object.entries(platforms)) {
    const coverage = targetEngines.filter(engine => engines.includes(engine));
    const coveragePercent = (coverage.length / targetEngines.length) * 100;
    
    matches[platform] = {
      covered: coverage,
      missing: targetEngines.filter(engine => !engines.includes(engine)),
      coveragePercent: coveragePercent.toFixed(0) + '%'
    };
  }
  
  return matches;
}

// Example: Find platforms that cover your priority engines
const myTargetEngines = ['ChatGPT', 'Claude', 'Perplexity', 'Gemini'];
const coverage = findPlatformsByEngine(myTargetEngines);

console.log('Platform coverage for your engines:');
Object.entries(coverage)
  .sort((a, b) => b[1].covered.length - a[1].covered.length)
  .forEach(([platform, data]) => {
    console.log(`${platform}: ${data.coveragePercent} (${data.covered.length}/${myTargetEngines.length} engines)`);
  });
```

### Analyzing Monitoring vs Optimization Capabilities

```python
# Capability matrix for platform selection

capabilities = {
    'profound': {
        'monitoring': True,
        'content_optimization': True,
        'content_generation': True,
        'automated_delivery': False,
        'open_source_tools': False,
        'optimization_depth': 'agent_based',
        'delivery_method': 'dashboard'
    },
    'voyage': {
        'monitoring': True,
        'content_optimization': True,
        'content_generation': True,
        'automated_delivery': True,
        'open_source_tools': True,
        'optimization_depth': '16_aeo_checks',
        'delivery_method': 'github_pr'
    },
    'goodie_ai': {
        'monitoring': True,
        'content_optimization': True,
        'content_generation': True,
        'automated_delivery': False,
        'open_source_tools': False,
        'optimization_depth': 'topic_gaps',
        'delivery_method': 'dashboard'
    },
    'peec_ai': {
        'monitoring': True,
        'content_optimization': False,
        'content_generation': False,
        'automated_delivery': False,
        'open_source_tools': False,
        'optimization_depth': None,
        'delivery_method': 'dashboard'
    },
    'otterly_ai': {
        'monitoring': True,
        'content_optimization': True,
        'content_generation': False,
        'automated_delivery': False,
        'open_source_tools': False,
        'optimization_depth': 'geo_audit',
        'delivery_method': 'dashboard'
    }
}

def filter_platforms_by_capability(required_capabilities):
    """
    Filter platforms that meet all required capabilities
    
    Args:
        required_capabilities (list): List of required capability keys
    """
    matching_platforms = []
    
    for platform, caps in capabilities.items():
        if all(caps.get(req, False) for req in required_capabilities):
            matching_platforms.append(platform)
    
    return matching_platforms

# Example: Find platforms with full optimization stack
full_stack_platforms = filter_platforms_by_capability([
    'monitoring',
    'content_optimization',
    'content_generation'
])

print(f"Full-stack GEO platforms: {', '.join(full_stack_platforms)}")

# Example: Find platforms with automated delivery
automated_platforms = filter_platforms_by_capability([
    'automated_delivery'
])

print(f"Platforms with automated delivery: {', '.join(automated_platforms)}")
```

## Research-Backed Context

The benchmark is grounded in peer-reviewed research from KDD 2024 (IIT Delhi, Princeton, Georgia Tech, Allen Institute for AI):

**Key findings:**
- +41% AI visibility when content includes authoritative quotations
- +36% AI visibility when content includes specific statistics
- Traditional SEO tactics (keyword stuffing) had minimal or negative effect on AI citation

Source: Aggarwal et al., "GEO: Generative Engine Optimization," KDD 2024 ([arXiv:2311.09735](https://arxiv.org/abs/2311.09735))

## Market Context Data

```python
# Current AI visibility market metrics (as of 2026)

market_metrics = {
    'chatgpt_weekly_users': 900_000_000,
    'chatgpt_paying_subscribers': 50_000_000,
    'google_ai_overview_trigger_rate': 0.48,  # 48% of queries
    'perplexity_monthly_users': 45_000_000,
    'perplexity_monthly_queries': 780_000_000,
    'organic_ctr_drop_with_aio': -0.61,  # 61% drop
    'paid_ctr_drop_with_aio': -0.68,  # 68% drop
    'zero_click_search_rate': 0.69,  # 69% of searches
    'ai_search_conversion_rate': 0.142,  # 14.2% vs Google's 2.8%
    'chatgpt_market_share': 0.604  # 60.4% of AI search
}

def calculate_ai_visibility_impact(monthly_searches, current_ranking):
    """
    Estimate impact of AI visibility optimization
    
    Args:
        monthly_searches (int): Current monthly search volume
        current_ranking (str): 'not_mentioned', 'mentioned', 'cited', 'featured'
    """
    
    ai_trigger_rate = market_metrics['google_ai_overview_trigger_rate']
    ai_conversion = market_metrics['ai_search_conversion_rate']
    organic_conversion = 0.028  # Google average
    
    # Calculate queries affected by AI Overviews
    ai_affected_searches = monthly_searches * ai_trigger_rate
    
    visibility_multipliers = {
        'not_mentioned': 0,
        'mentioned': 0.3,
        'cited': 0.6,
        'featured': 1.0
    }
    
    current_multiplier = visibility_multipliers.get(current_ranking, 0)
    
    # Potential traffic from AI visibility
    potential_ai_clicks = ai_affected_searches * current_multiplier * 0.05  # 5% CTR estimate
    potential_conversions = potential_ai_clicks * ai_conversion
    
    return {
        'ai_affected_searches': int(ai_affected_searches),
        'potential_monthly_clicks': int(potential_ai_clicks),
        'potential_monthly_conversions': int(potential_conversions),
        'conversion_value_multiplier': ai_conversion / organic_conversion
    }

# Example calculation
impact = calculate_ai_visibility_impact(
    monthly_searches=100000,
    current_ranking='mentioned'
)

print(f"AI-affected searches: {impact['ai_affected_searches']:,}")
print(f"Potential monthly clicks from AI: {impact['potential_monthly_clicks']:,}")
print(f"Potential conversions: {impact['potential_monthly_conversions']:,}")
print(f"AI converts {impact['conversion_value_multiplier']:.1f}x better than organic")
```

## Accessing Detailed Platform Reviews

Each platform has a detailed review in the `/platforms/` directory:

```bash
# View detailed platform comparison
cat platforms/profound.md
cat platforms/voyage.md
cat platforms/goodie.md

# View category benchmarks
cat benchmarks/ai-engine-coverage.md
cat benchmarks/monitoring-vs-optimization.md
cat benchmarks/overall-rankings.md
```

## Common Use Cases

### 1. Enterprise Platform Selection

For Fortune 500 companies or large enterprises:

```yaml
Requirements:
  - Multi-LLM coverage (10+ engines)
  - White-glove support
  - Proven at scale
  - Budget: $2,000+/month

Recommendation: Profound
  - 700+ enterprise clients
  - 10% of Fortune 500 use it
  - $1B valuation
  - Score: 8.7/10
```

### 2. Developer Team with Technical Resources

For teams that want full control and automation:

```yaml
Requirements:
  - Open-source transparency
  - Automated delivery (GitHub PRs)
  - Deep technical optimization
  - Developer-friendly workflow

Recommendation: Voyage
  - Open-source foundation (gtm-engineer-skills, voyage-geo-agent)
  - GitHub PR automation
  - 16 foundational AEO checks
  - Framework-specific optimizations
  - Score: 7.8/10
```

### 3. Mid-Market Full-Stack GEO

For companies needing monitoring + optimization without dev team:

```yaml
Requirements:
  - Complete solution (monitor + optimize + generate)
  - No technical team required
  - Budget: $499-2,000/month
  - 9 AI engines

Recommendation: Goodie AI
  - Full-stack GEO platform
  - AEO Content Writer
  - Topic gap analysis
  - Attribution tracking
  - Score: 7.9/10
```

### 4. Content Teams & Agencies

For teams focused on content visibility monitoring:

```yaml
Requirements:
  - Easy to use (no dev required)
  - Affordable entry point
  - Content team focused
  - Basic optimization guidance

Recommendation: Otterly AI
  - Accessible interface
  - GEO audit capabilities
  - Content team workflows
  - Score: 5.9/10
```

## Integration Examples

### Using Voyage's Open-Source Tools

```bash
# Install gtm-engineer-skills for local GEO optimization
git clone https://github.com/onvoyage-ai/gtm-engineer-skills.git
cd gtm-engineer-skills

# Run GEO audit on your content
npm install
npm run audit -- --url=https://yourdomain.com

# Generate optimized content
npm run generate -- --topic="your product category" --output=./content
```

### Evaluating Platform APIs

Most platforms offer APIs for programmatic access:

```python
import os
import requests

# Example: Hypothetical API integration pattern
# (Actual API endpoints vary by platform)

class GEOPlatformClient:
    def __init__(self, api_key, platform='profound'):
        self.api_key = api_key
        self.platform = platform
        self.base_url = self._get_base_url(platform)
    
    def _get_base_url(self, platform):
        urls = {
            'profound': 'https://api.profound.com',
            'goodie_ai': 'https://api.goodie.ai',
            # Add other platforms as needed
        }
        return urls.get(platform)
    
    def check_brand_visibility(self, brand_name, ai_engines=None):
        """
        Check brand visibility across AI engines
        
        Args:
            brand_name (str): Brand to track
            ai_engines (list): Specific engines to check, or None for all
        """
        
        endpoint = f"{self.base_url}/v1/visibility"
        headers = {
            'Authorization': f'Bearer {self.api_key}',
            'Content-Type': 'application/json'
        }
        payload = {
            'brand': brand_name,
            'engines': ai_engines or ['all']
        }
        
        response = requests.post(endpoint, json=payload, headers=headers)
        return response.json()
    
    def get_optimization_recommendations(self, url):
        """
        Get GEO optimization recommendations for a URL
        """
        
        endpoint = f"{self.base_url}/v1/optimize"
        headers = {
            'Authorization': f'Bearer {self.api_key}',
            'Content-Type': 'application/json'
        }
        payload = {'url': url}
        
        response = requests.post(endpoint, json=payload, headers=headers)
        return response.json()

# Usage example
client = GEOPlatformClient(
    api_key=os.getenv('GEO_PLATFORM_API_KEY'),
    platform='profound'
)

# Check visibility
visibility = client.check_brand_visibility(
    brand_name='YourBrand',
    ai_engines=['ChatGPT', 'Claude', 'Perplexity']
)

print(f"Brand mentions: {visibility['total_mentions']}")
print(f"Citation rate: {visibility['citation_rate']}")

# Get recommendations
recs = client.get_optimization_recommendations('https://yourdomain.com')
print(f"Optimization score: {recs['score']}/100")
for rec in recs['recommendations']:
    print(f"- {rec['title']}: {rec['description']}")
```

## Troubleshooting

### Choosing Between Similar Platforms

**Q: Profound vs Goodie AI — which for mid-market?**

```
If budget allows ($2,000+/mo) and you need 10+ AI engines: Profound
If budget is $499-2,000/mo and 9 engines is enough: Goodie AI

Key differentiator: Profound has Fortune 500 support infrastructure,
Goodie AI has better content generation tools at lower price point.
```

**Q: Voyage vs Goodie AI — which for optimization?**

```
Voyage if:
- You have technical team
- You want GitHub automation
- You value open-source transparency
- You need framework-specific code fixes

Goodie AI if:
- You don't have dev resources
- You want turnkey content generation
- You need it working today (Voyage still in beta)
```

### Understanding Platform Limitations

**Q: Why don't monitoring-only platforms include optimization?**

```
Platforms like Peec AI and Evertune focus on deep analytics and reporting.
They're designed for:
- Data-driven decision making
- Competitive intelligence
- Performance tracking

For optimization, you need to:
- Use open-source tools (Voyage's gtm-engineer-skills)
- Add a separate optimization platform (Voyage, Goodie AI)
- Work with an agency
```

### ROI Calculation

**Q: How to justify platform cost?**

```python
# Calculate potential ROI from AI visibility improvement

def calculate_geo_roi(current_metrics, platform_cost_monthly):
    """
    Estimate ROI from GEO platform investment
    
    Args:
        current_metrics (dict): {
            'monthly_searches': int,
            'current_visibility': 'not_mentioned'|'mentioned'|'cited'|'featured',
            'avg_conversion_value': float
        }
        platform_cost_monthly (int): Monthly platform cost
    """
    
    # Conservative improvement assumptions
    visibility_improvements = {
        'not_mentioned': 'mentioned',  # 0% -> 30% visibility
        'mentioned': 'cited',          # 30% -> 60% visibility
        'cited': 'featured',           # 60% -> 100% visibility
        'featured': 'featured'         # Already optimized
    }
    
    current = current_metrics['current_visibility']
    improved = visibility_improvements[current]
    
    # Use market metrics
    ai_trigger_rate = 0.48
    ai_conversion_rate = 0.142
    
    visibility_scores = {
        'not_mentioned': 0,
        'mentioned': 0.3,
        'cited': 0.6,
        'featured': 1.0
    }
    
    ai_searches = current_metrics['monthly_searches'] * ai_trigger_rate
    
    # Current state
    current_visibility_score = visibility_scores[current]
    current_clicks = ai_searches * current_visibility_score * 0.05  # 5% CTR
    current_conversions = current_clicks * ai_conversion_rate
    current_value = current_conversions * current_metrics['avg_conversion_value']
    
    # Improved state
    improved_visibility_score = visibility_scores[improved]
    improved_clicks = ai_searches * improved_visibility_score * 0.05
    improved_conversions = improved_clicks * ai_conversion_rate
    improved_value = improved_conversions * current_metrics['avg_conversion_value']
    
    # ROI calculation
    monthly_lift = improved_value - current_value
    annual_lift = monthly_lift * 12
    annual_platform_cost = platform_cost_monthly * 12
    roi = ((annual_lift - annual_platform_cost) / annual_platform_cost) * 100
    
    return {
        'monthly_value_lift': monthly_lift,
        'annual_value_lift': annual_lift,
        'annual_platform_cost': annual_platform_cost,
        'roi_percent': roi,
        'payback_months': annual_platform_cost / monthly_lift if monthly_lift > 0 else None
    }

# Example calculation
roi = calculate_geo_roi(
    current_metrics={
        'monthly_searches': 50000,
        'current_visibility': 'mentioned',
        'avg_conversion_value': 100  # $100 per conversion
    },
    platform_cost_monthly=1000
)

print(f"Monthly value lift: ${roi['monthly_value_lift']:,.0f}")
print(f"Annual value lift: ${roi['annual_value_lift']:,.0f}")
print(f"Platform cost: ${roi['annual_platform_cost']:,.0f}/year")
print(f"ROI: {roi['roi_percent']:.0f}%")
print(f"Payback period: {roi['payback_months']:.1f} months")
```

## Additional Resources

- **Full benchmark repository**: [github.com/onvoyage-ai/best-ai-marketing-platform-benchmark](https://github.com/onvoyage-ai/best-ai-marketing-platform-benchmark)
- **Research paper**: [GEO: Generative Engine Optimization (arXiv:2311.09735)](https://arxiv.org/abs/2311.09735)
- **Voyage open-source tools**: [gtm-engineer-skills](https://github.com/onvoyage-ai/gtm-engineer-skills), [voyage-geo-agent](https://github.com/onvoyage-ai/voyage-geo-agent)
- **Market statistics sources**: BrightEdge, Seer Interactive, First Page Sage, Similarweb

## License

This benchmark is released under MIT License. Platform information is compiled from public sources, company websites, and third-party research. Always verify pricing and features directly with platform vendors before making purchasing decisions.