Mavera Documentation
  • Introduction to Mavera
    • About Mavera
    • Our mission and vision
  • Our Technology
    • Overview of Mavera's AI Ecosystem
    • What Makes us Different from ChatGPT
  • Our Frameworks
    • Ellie: The Orchestrator
    • Emma: The Adversarial System
    • Gremlins: Data Harvesters
    • Sprites: Data Annotators
    • Personas: Targeted AI Swarms
    • Heracles: Individual Customer Modeling
  • Key Concepts
    • What is a Persona?
    • Core Technology: Mavera's AI Personas
    • AI Personas vs. Traditional Personas
    • Parallels with Traditional Personas
    • How Our AI Personas Work
    • Determining AI Persona Sample Size
    • Understanding AI Swarms
    • Hard to Reach Audiences
    • The Role of Data Scraping and Annotation
    • Synthetic Data Generation
    • The Emotional Intelligence of Our AI in Marketing
  • Privacy and Ethics
    • Data Handling and Privacy Policies
    • Ethical AI Development and Usage
  • FAQs and Support
    • Frequently Asked Questions
    • Contact Support
    • Troubleshooting Guide
  • The AI Revolution in Marketing: Why You Need It
  • Benefits of Mavera's AI Personas
  • ⚒️Use Cases
    • Our Offerings Overview
    • Qualitative Customer Research and Insights
      • Qualitative Research: Example Output
    • Individual Customer Profiling and Segmentation
      • Customer Profiling: Example Output
    • Competitor Analysis and Market Research
      • Competitor Analysis: Example Output
    • Content Analysis and Sentiment Tracking
      • Content Analysis: Example Output
    • Keyword Research and Topic Discovery
      • Keyword Research: Example Output
    • Creative Ideation and Testing
      • Creative Ideation: Example Output
    • Predictive Analytics and Trend Forecasting
      • Predictive Analytics: Example Output
    • Personalized Content Creation and Targeting
      • Personalized Content: Example Output
    • Brand Perception and Reputation Management
      • Brand Perception: Example Output
    • Customer Journey Mapping and Optimization
      • Customer Journey: Example Output
    • Enhancing Existing Market Research
      • Enhancing Market Research: Example Output
    • Influencer Identification and Analysis
      • Influencer Identification: Example Output
    • Customer Churn Prediction and Prevention
      • Customer Churn Prediction: Example Output
    • Pricing Optimization and Elasticity Analysis
      • Pricing Optimization: Example Output
    • Product Feature Prioritization
      • Product Feature Prioritization: Example Output
    • Marketing Mix Modeling and Optimization
      • Marketing Mix Modeling: Example Output
    • Ad Creative Testing and Optimization
      • Ad Creative Testing: Example Output
  • Case Study: AI Persona vs. Deloitte Study
  • AI Search Engine Optimization
  • Handling 'Practical' Jobs: Mavera's Advanced Approach
  • Quality Assurance in AI Outputs: Volume-Driven
  • The State of AI in Marketing
  • Mavera's Unique Advantage
  • ROI of AI in Marketing
  • The 'Destination': Future of AI in Marketing
  • Getting Started with Mavera
  • Fast Food Questions
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  • Key Features
  • How Gremlins Work
  • Benefits
  1. Our Frameworks

Gremlins: Data Harvesters

Learn about Gremlins, Mavera's advanced data scraping system. Discover how these AI-powered agents collect large volumes of balanced, relevant data to fuel our customer insight engine.

Gremlins are Mavera's sophisticated data collection agents, designed to efficiently gather large volumes of data from specific sources while maintaining a balance in sentiment and perspective.

Key Features

  1. Source-Specific Scraping: Each Gremlin is created for a specific website or data source, allowing for optimized data collection.

  2. AI-Powered Navigation: Gremlins use AI-informed copies of tools like Selenium and Puppeteer to navigate and extract data efficiently.

  3. Balanced Data Collection: Gremlins are programmed to seek out sources with balanced sentiment, ensuring a diverse and unbiased dataset.

  4. High-Speed Processing: On high-performance hardware, Gremlins can process approximately 800,000 data sources per hour.

  5. Adaptive Scraping: Gremlins can adjust their scraping strategies based on the structure and content of each source.

How Gremlins Work

  1. Gremlins receive instructions from Ellie or human users about which sources to scrape, including any subdomains.

  2. They navigate to the specified sources using AI-enhanced web scraping tools.

  3. Gremlins extract relevant data, continuously assessing the sentiment balance of the collected information.

  4. The scraped data is then processed and stored for further analysis by other components of the Mavera ecosystem.

Benefits

  • Comprehensive Data Collection: Gremlins ensure that no relevant data is missed, providing a solid foundation for analysis.

  • Efficiency: The high-speed processing capabilities of Gremlins allow for rapid data collection and analysis.

  • Balanced Perspective: By seeking out diverse sentiments, Gremlins help prevent bias in the collected data.

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Last updated 11 months ago