Tech GEO
Crawler Optimization
Optimize your Site for Crawlers
Azoma provides comprehensive site auditing, automated optimization, and crawler analytics to maximize your AI search visibility.
Tech GEO
Crawler Optimization
Optimize your site for Crawlers
Azoma provides comprehensive site auditing, automated optimization, and crawler analytics to maximize your AI search visibility.
Tech GEO
Crawler Optimization
Optimize your Site for Crawlers
Azoma provides comprehensive site auditing, automated optimization, and crawler analytics to maximize your AI search visibility.
Site Readability
Site Auditing & Optimization
Site Auditing & Optimization
Run comprehensive technical audits and implement automated optimizations to ensure your site is fully prepared for AI search discovery.
Bot Allowance
Sitemap
Crawling…
Page Speed
Accessability
Status:
Crawling
Robots.txt / Sitemap.xml / Schema.org
Access & Direction Analysis
Access & Direction Analysis
Audit robots.txt files for AI crawler allowance, validate sitemap.xml structure and accessibility, and check for proper llms.txt implementation. Our platform identifies configuration issues that prevent AI engines from accessing and understanding your site structure.
Javascript / HTML
Crawler Readability Assessment
Crawler Readability Assessment
Scan HTML structure over JavaScript implementation to ensure AI engines can effectively parse your content. Identify meta data implementation across pages and analyze structured data presence to optimize AI understanding of your content.
- class SchemaChecker:def __init__(self, required_fields):self.required_fields = required_fieldsself.status = "pending"def check_schema(self, data):missing_fields = []for field in self.required_fields:if field not in data or data[field] is None:missing_fields.append(field)if missing_fields:self.status = "invalid"return f"Schema validation failed! Missing: {', '.join(missing_fields)}"else:self.status = "valid"return "Schema validation passed!"def get_status(self):return f"Status: {self.status}"
- class SchemaChecker:def __init__(self, required_fields):self.required_fields = required_fieldsself.status = "pending"def check_schema(self, data):missing_fields = []for field in self.required_fields:if field not in data or data[field] is None:missing_fields.append(field)if missing_fields:self.status = "invalid"return f"Schema validation failed! Missing: {', '.join(missing_fields)}"else:self.status = "valid"return "Schema validation passed!"def get_status(self):return f"Status: {self.status}"
- class SchemaChecker:def __init__(self, required_fields):self.required_fields = required_fieldsself.status = "pending"def check_schema(self, data):missing_fields = []for field in self.required_fields:if field not in data or data[field] is None:missing_fields.append(field)if missing_fields:self.status = "invalid"return f"Schema validation failed! Missing: {', '.join(missing_fields)}"else:self.status = "valid"return "Schema validation passed!"def get_status(self):return f"Status: {self.status}"
- class SchemaChecker:def __init__(self, required_fields):self.required_fields = required_fieldsself.status = "pending"def check_schema(self, data):missing_fields = []for field in self.required_fields:if field not in data or data[field] is None:missing_fields.append(field)if missing_fields:self.status = "invalid"return f"Schema validation failed! Missing: {', '.join(missing_fields)}"else:self.status = "valid"return "Schema validation passed!"def get_status(self):return f"Status: {self.status}"
- class SchemaChecker:def __init__(self, required_fields):self.required_fields = required_fieldsself.status = "pending"def check_schema(self, data):missing_fields = []for field in self.required_fields:if field not in data or data[field] is None:missing_fields.append(field)if missing_fields:self.status = "invalid"return f"Schema validation failed! Missing: {', '.join(missing_fields)}"else:self.status = "valid"return "Schema validation passed!"def get_status(self):return f"Status: {self.status}"
- class SchemaChecker:def __init__(self, required_fields):self.required_fields = required_fieldsself.status = "pending"def check_schema(self, data):missing_fields = []for field in self.required_fields:if field not in data or data[field] is None:missing_fields.append(field)if missing_fields:self.status = "invalid"return f"Schema validation failed! Missing: {', '.join(missing_fields)}"else:self.status = "valid"return "Schema validation passed!"def get_status(self):return f"Status: {self.status}"
- class SchemaChecker:def __init__(self, required_fields):self.required_fields = required_fieldsself.status = "pending"def check_schema(self, data):missing_fields = []for field in self.required_fields:if field not in data or data[field] is None:missing_fields.append(field)if missing_fields:self.status = "invalid"return f"Schema validation failed! Missing: {', '.join(missing_fields)}"else:self.status = "valid"return "Schema validation passed!"def get_status(self):return f"Status: {self.status}"
- class SchemaChecker:def __init__(self, required_fields):self.required_fields = required_fieldsself.status = "pending"def check_schema(self, data):missing_fields = []for field in self.required_fields:if field not in data or data[field] is None:missing_fields.append(field)if missing_fields:self.status = "invalid"return f"Schema validation failed! Missing: {', '.join(missing_fields)}"else:self.status = "valid"return "Schema validation passed!"def get_status(self):return f"Status: {self.status}"
- class HTMLReadabilityChecker:def __init__(self, page_url= page_url):self.page_url = page_urlself.status = "pending"def scan_readability(self, page_content):
js_dependent_elements = []html_content = page_content.get('html_text', '')js_rendered = page_content.get('js_content', '')if len(html_content) < len(js_rendered) * 0.6:js_dependent_elements.append("main_content")if not page_content.get('meta_tags'):js_dependent_elements.append("meta_data")if js_dependent_elements:self.status = "**poor_readability**"return f"**Readability** scan failed! JS-dependent: {', '.join(js_dependent_elements)}"else:self.status = "**good_readability**" return "**Readability** scan passed!" - class HTMLReadabilityChecker:def __init__(self, page_url= page_url):self.page_url = page_urlself.status = "pending"def scan_readability(self, page_content):
js_dependent_elements = []html_content = page_content.get('html_text', '')js_rendered = page_content.get('js_content', '')if len(html_content) < len(js_rendered) * 0.6:js_dependent_elements.append("main_content")if not page_content.get('meta_tags'):js_dependent_elements.append("meta_data")if js_dependent_elements:self.status = "**poor_readability**"return f"**Readability** scan failed! JS-dependent: {', '.join(js_dependent_elements)}"else:self.status = "**good_readability**" return "**Readability** scan passed!" - class HTMLReadabilityChecker:def __init__(self, page_url= page_url):self.page_url = page_urlself.status = "pending"def scan_readability(self, page_content):
js_dependent_elements = []html_content = page_content.get('html_text', '')js_rendered = page_content.get('js_content', '')if len(html_content) < len(js_rendered) * 0.6:js_dependent_elements.append("main_content")if not page_content.get('meta_tags'):js_dependent_elements.append("meta_data")if js_dependent_elements:self.status = "**poor_readability**"return f"**Readability** scan failed! JS-dependent: {', '.join(js_dependent_elements)}"else:self.status = "**good_readability**" return "**Readability** scan passed!"
Generate
Schema
LLMS.txt
Robots.txt
Generate
Schema
LLMS.txt
Robots.txt
Generate
Schema
LLMS.txt
Robots.txt
Generate
Schema
LLMS.txt
Robots.txt
Generate
Schema
LLMS.txt
Robots.txt
Ease of Use
Automated Technical Generation
Automated Technical Generation
Generate optimized robots.txt rules, llms.txt files from your sitemap and user input, and missing meta data including schema markup. Our platform creates properly formatted technical elements while retaining your current security restrictions and protocols.
Crawler Analytics
Crawler Analytics & Monitoring
Crawler Analytics & Monitoring
Stay ahead with comprehensive analysis of how your brand compares across all major AI platforms. Track share of voice changes and identify growth opportunities.
Crawl Log
Real-Time Crawler Monitoring
See how often AI crawlers are visiting your site and which pages are getting indexed across different AI platforms. Track crawler visit frequency and monitor crawling patterns to identify optimization opportunities based on actual AI engine behavior.
Crawl Logs
Last Crawled:
ChatGPT
Last Crawled: Today 12:56
Details
Perplexity
Last Crawled: Today 11:46
Details
Gemini
Last Crawled: Tues 22:50
Details
Google
Last Crawled: Tues 22:17
Details
Claude
Last Crawled: Mon 23:59
Details
Referral Traffic
AI Traffic Analysis
Measure AI traffic percentages across your site and connect to GA4 to analyze referral traffic from AI search engines. Get comprehensive insights into how your technical optimizations translate into measurable traffic and engagement from AI platforms.
Measure AI traffic percentages across your site and connect to GA4 to analyze referral traffic from AI search engines. Get comprehensive insights into how your technical optimizations translate into measurable traffic and engagement from AI platforms.
Referral Traffic
24.7%
Oct
Nov
Dec
Jan
Feb
Mar
Apr
Integrations
Core Platform Integrations
Core Platform Integrations
Connect our technical optimization capabilities with your existing analytics and site management infrastructure for comprehensive monitoring and seamless implementation.
Agentic Recommendations
Get actionable recommendations from your performance data in one click. Receive priority-scored insights integrated directly into Slack and Teams workflows.
Analytics Integration
Connect GA4 and Google Search Console to correlate AI performance with traffic and conversions. Measure the business impact of AI visibility investments.
Workspace Integration
Get AI insights and alerts directly in Slack or Teams. Share intelligence and coordinate optimization activities within existing workflows.
Opportunity Detection
We actively monitor Reddit, Quora, Wikipedia, and other forums for citation opportunities. Get alerts when discussions emerge where your participation could influence AI training data.
Analytics Integration
Connect Google Analytics 4 (GA4) for comprehensive traffic analysis and AI referral tracking. Integrate Google Search Console (GSC) for search performance monitoring and technical issue identification. Access real-time crawler monitoring dashboards with detailed reporting on AI platform interactions.
Workspace Integration
Connect with Slack and Teams for automated alerts on crawler activity, technical issues, and optimization opportunities. Receive notifications when new technical barriers are identified or when AI crawler patterns change significantly across your site.
Workspace Integration
Connect with Slack and Teams for automated alerts on crawler activity, technical issues, and optimization opportunities. Receive notifications when new technical barriers are identified or when AI crawler patterns change significantly across your site.
Analytics Integration
Connect Google Analytics 4 (GA4) for comprehensive traffic analysis and AI referral tracking. Integrate Google Search Console (GSC) for search performance monitoring and technical issue identification. Access real-time crawler monitoring dashboards with detailed reporting on AI platform interactions.
Take control of the AI Conversation
Gain visibility, generate content to influence AI Engines and ensure you are being represented accurately with Azoma
Take control of the AI Conversation
Gain visibility, generate content to influence AI Engines and ensure you are being represented accurately with Azoma
Take control of the AI Conversation
Gain visibility, generate content to influence AI Engines and ensure you are being represented accurately with Azoma
FAQs
Crawler Questions
Our most asked questions about AI Crawlers
FAQs
Crawler Questions
Can AI crawlers read JavaScript?
AI crawlers have evolved significantly, yet most major ones still struggle to fully execute JavaScript. This limitation means that dynamic content generated by frameworks like React or Angular may not be visible to these crawlers, impacting the accuracy of AI-driven search results. To ensure comprehensive data collection, it's crucial to implement server-side rendering or prerendering solutions, making essential information accessible in the initial HTML response.
Does llms.txt actually work?
Potentially. The llms.txt file, a proposed standard for guiding Large Language Models (LLMs) to specific content on websites, is not yet widely adopted, with its effectiveness still debated. While some argue it enhances content visibility and accuracy for AI searches, others see it as non-standard and potentially redundant. Despite this, companies like Vercel and Anthropic have started using llms.txt, indicating its potential role in future AI interactions.
Is schema markup important for chatgpt?
Schema markup is crucial for ChatGPT and other AI-powered search engines, as it helps them understand website content more effectively, leading to improved search results and accurate responses. By providing structured data, schema markup clarifies content meaning, enhancing AI's ability to grasp context and purpose. This results in better visibility in AI-driven searches and more relevant user query answers.
How often do AI crawlers actually visit websites?
AI crawlers visit websites with varying frequency, influenced by factors like update frequency, importance, and purpose. High-frequency sites, like news or e-commerce platforms, may be crawled multiple times daily, while static sites might see monthly visits. Understanding these patterns helps optimize site visibility and performance.
How do I track how often AI crawlers are visiting my site?
To track AI crawler visits to your site, utilize web analytics tools and techniques for identifying AI traffic. Google Analytics, Google Search Console, and platforms like SEMrush or Ahrefs offer insights into traffic patterns, including AI activity. Azoma's Crawler Analytics can specifically monitor AI bot behavior, helping you understand which platforms visit your site and how often.
Can AI crawlers read JavaScript?
AI crawlers have evolved significantly, yet most major ones still struggle to fully execute JavaScript. This limitation means that dynamic content generated by frameworks like React or Angular may not be visible to these crawlers, impacting the accuracy of AI-driven search results. To ensure comprehensive data collection, it's crucial to implement server-side rendering or prerendering solutions, making essential information accessible in the initial HTML response.
Does llms.txt actually work?
Potentially. The llms.txt file, a proposed standard for guiding Large Language Models (LLMs) to specific content on websites, is not yet widely adopted, with its effectiveness still debated. While some argue it enhances content visibility and accuracy for AI searches, others see it as non-standard and potentially redundant. Despite this, companies like Vercel and Anthropic have started using llms.txt, indicating its potential role in future AI interactions.
Is schema markup important for chatgpt?
Schema markup is crucial for ChatGPT and other AI-powered search engines, as it helps them understand website content more effectively, leading to improved search results and accurate responses. By providing structured data, schema markup clarifies content meaning, enhancing AI's ability to grasp context and purpose. This results in better visibility in AI-driven searches and more relevant user query answers.
How often do AI crawlers actually visit websites?
AI crawlers visit websites with varying frequency, influenced by factors like update frequency, importance, and purpose. High-frequency sites, like news or e-commerce platforms, may be crawled multiple times daily, while static sites might see monthly visits. Understanding these patterns helps optimize site visibility and performance.
How do I track how often AI crawlers are visiting my site?
To track AI crawler visits to your site, utilize web analytics tools and techniques for identifying AI traffic. Google Analytics, Google Search Console, and platforms like SEMrush or Ahrefs offer insights into traffic patterns, including AI activity. Azoma's Crawler Analytics can specifically monitor AI bot behavior, helping you understand which platforms visit your site and how often.
Our most asked questions about AI crawlers
Can AI crawlers read JavaScript?
AI crawlers have evolved significantly, yet most major ones still struggle to fully execute JavaScript. This limitation means that dynamic content generated by frameworks like React or Angular may not be visible to these crawlers, impacting the accuracy of AI-driven search results. To ensure comprehensive data collection, it's crucial to implement server-side rendering or prerendering solutions, making essential information accessible in the initial HTML response.
Does llms.txt actually work?
Potentially. The llms.txt file, a proposed standard for guiding Large Language Models (LLMs) to specific content on websites, is not yet widely adopted, with its effectiveness still debated. While some argue it enhances content visibility and accuracy for AI searches, others see it as non-standard and potentially redundant. Despite this, companies like Vercel and Anthropic have started using llms.txt, indicating its potential role in future AI interactions.
Is schema markup important for chatgpt?
Schema markup is crucial for ChatGPT and other AI-powered search engines, as it helps them understand website content more effectively, leading to improved search results and accurate responses. By providing structured data, schema markup clarifies content meaning, enhancing AI's ability to grasp context and purpose. This results in better visibility in AI-driven searches and more relevant user query answers.
How often do AI crawlers actually visit websites?
AI crawlers visit websites with varying frequency, influenced by factors like update frequency, importance, and purpose. High-frequency sites, like news or e-commerce platforms, may be crawled multiple times daily, while static sites might see monthly visits. Understanding these patterns helps optimize site visibility and performance.
How do I track how often AI crawlers are visiting my site?
To track AI crawler visits to your site, utilize web analytics tools and techniques for identifying AI traffic. Google Analytics, Google Search Console, and platforms like SEMrush or Ahrefs offer insights into traffic patterns, including AI activity. Azoma's Crawler Analytics can specifically monitor AI bot behavior, helping you understand which platforms visit your site and how often.
Can AI crawlers read JavaScript?
AI crawlers have evolved significantly, yet most major ones still struggle to fully execute JavaScript. This limitation means that dynamic content generated by frameworks like React or Angular may not be visible to these crawlers, impacting the accuracy of AI-driven search results. To ensure comprehensive data collection, it's crucial to implement server-side rendering or prerendering solutions, making essential information accessible in the initial HTML response.
Does llms.txt actually work?
Potentially. The llms.txt file, a proposed standard for guiding Large Language Models (LLMs) to specific content on websites, is not yet widely adopted, with its effectiveness still debated. While some argue it enhances content visibility and accuracy for AI searches, others see it as non-standard and potentially redundant. Despite this, companies like Vercel and Anthropic have started using llms.txt, indicating its potential role in future AI interactions.
Is schema markup important for chatgpt?
Schema markup is crucial for ChatGPT and other AI-powered search engines, as it helps them understand website content more effectively, leading to improved search results and accurate responses. By providing structured data, schema markup clarifies content meaning, enhancing AI's ability to grasp context and purpose. This results in better visibility in AI-driven searches and more relevant user query answers.
How often do AI crawlers actually visit websites?
AI crawlers visit websites with varying frequency, influenced by factors like update frequency, importance, and purpose. High-frequency sites, like news or e-commerce platforms, may be crawled multiple times daily, while static sites might see monthly visits. Understanding these patterns helps optimize site visibility and performance.
How do I track how often AI crawlers are visiting my site?
To track AI crawler visits to your site, utilize web analytics tools and techniques for identifying AI traffic. Google Analytics, Google Search Console, and platforms like SEMrush or Ahrefs offer insights into traffic patterns, including AI activity. Azoma's Crawler Analytics can specifically monitor AI bot behavior, helping you understand which platforms visit your site and how often.
Can AI crawlers read JavaScript?
AI crawlers have evolved significantly, yet most major ones still struggle to fully execute JavaScript. This limitation means that dynamic content generated by frameworks like React or Angular may not be visible to these crawlers, impacting the accuracy of AI-driven search results. To ensure comprehensive data collection, it's crucial to implement server-side rendering or prerendering solutions, making essential information accessible in the initial HTML response.
Does llms.txt actually work?
Potentially. The llms.txt file, a proposed standard for guiding Large Language Models (LLMs) to specific content on websites, is not yet widely adopted, with its effectiveness still debated. While some argue it enhances content visibility and accuracy for AI searches, others see it as non-standard and potentially redundant. Despite this, companies like Vercel and Anthropic have started using llms.txt, indicating its potential role in future AI interactions.
Is schema markup important for chatgpt?
Schema markup is crucial for ChatGPT and other AI-powered search engines, as it helps them understand website content more effectively, leading to improved search results and accurate responses. By providing structured data, schema markup clarifies content meaning, enhancing AI's ability to grasp context and purpose. This results in better visibility in AI-driven searches and more relevant user query answers.
How often do AI crawlers actually visit websites?
AI crawlers visit websites with varying frequency, influenced by factors like update frequency, importance, and purpose. High-frequency sites, like news or e-commerce platforms, may be crawled multiple times daily, while static sites might see monthly visits. Understanding these patterns helps optimize site visibility and performance.
How do I track how often AI crawlers are visiting my site?
To track AI crawler visits to your site, utilize web analytics tools and techniques for identifying AI traffic. Google Analytics, Google Search Console, and platforms like SEMrush or Ahrefs offer insights into traffic patterns, including AI activity. Azoma's Crawler Analytics can specifically monitor AI bot behavior, helping you understand which platforms visit your site and how often.
Lead the AI shift. Or get lost in it
Future-Proof Your Brand for AI Search
Ensure you are seen above your competitors in AI-powered search, across ChatGPT, Amazon RUFUS, Perplexity, Google AI Overviews, with our end-to-end solution. Be first to market to capture demand from billions of high-intent queries.
Prepare Your Brand
Future-Proof Your Brand for AI Search
Ensure you are seen above your competitors in AI-powered search, across ChatGPT, Amazon RUFUS, Perplexity, Google AI Overviews, with our end-to-end solution. Be first to market to capture demand from billions of high-intent queries.
Lead the AI shift. Or get lost in it
Future-Proof Your Brand for AI Search
Ensure you are seen above your competitors in AI-powered search, across ChatGPT, Amazon RUFUS, Perplexity, Google AI Overviews, with our end-to-end solution. Be first to market to capture demand from billions of high-intent queries.