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 Sprites Work
  • Benefits
  1. Our Frameworks

Sprites: Data Annotators

Explore Sprites, Mavera's intelligent data annotation system. Learn how these AI-powered tools add context and meaning to raw data, enabling deeper customer insights and more accurate modeling.

Sprites are Mavera's advanced data annotation tools, leveraging Natural Language Processing (NLP) and custom transformer models to add context, grounding, and honesty to our AI system.

Key Features

  1. Flexible Annotation: Sprites can perform a wide range of annotation tasks, from sentiment analysis to topic extraction.

  2. Custom Transformer Models: Mavera's proprietary transformer models allow for highly accurate and context-aware annotations.

  3. Multi-Level Processing: Sprites operate at both base and advanced levels, with more complex annotations performed on-demand.

  4. Contextual Understanding: Sprites can understand and annotate data within its broader context, not just isolated data points.

  5. Targeted Analysis: Sprites can determine which customer bases are most likely to be interested in specific topics or events.

How Sprites Work

  1. Base Sprite processing is run on all data points ingested by the Gremlins.

  2. More advanced, topic-specific Sprites are activated on-demand based on the task requirements.

  3. Sprites use a combination of NLP techniques and custom transformer models to analyze and annotate the data.

  4. Annotations can include political sentiment, keyword extraction, topic identification, and follow-up event prediction.

  5. The annotated data is then made available to other components of the Mavera ecosystem, particularly Personas and Heracles.

Benefits

  • Deep Insights: Sprites' annotations add layers of meaning to raw data, enabling deeper insights.

  • Contextual Awareness: By understanding context, Sprites help create more accurate and nuanced customer models.

  • Flexibility: The ability to customize Sprites for specific annotation tasks allows for adaptability to various business needs.

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Last updated 11 months ago