Annotating Web3 & Cryptocurrency Data for an AI Startup

A U.S.based AI company building intelligent systems for the Web3 space needed high-quality labeled data to train and fine-tune its machine learning models. Their use case involved parsing, categorizing, and understanding blockchain-related text - including crypto transactions, smart contract descriptions, and community conversations.

Femote was subcontracted to deliver consistent and accurate annotations across complex, domain-specific content.

The project required us to:

1. Label Web3-related text data from social media pages, identifying client-specified entities.
2. Categorize content by topic and intent.
   
3. Maintain terminology consistency across emerging and fast-changing Web3 language.

• Domain-Aligned Team
  We selected annotators with experience in blockchain, crypto, and fintech to ensure contextual understanding.
  
• Custom Labeling Guidelines
  Collaborated with the client to define clear annotation rules, edge cases, and examples, adapting as new terms emerged.
• QA Workflow
  Integrated a multi-step quality control process, including QA review and regular audits, to ensure accuracy and adherence to guidelines.

• 10,000+ crypto-related data points labeled across 6 annotation types
• 98.5% consistency score based on client's in-house model validation
• Faster time-to-train: Improved initial training speed by providing structured data ahead of schedule
• Model performance uplift: Labeled data contributed to a 12% improvement in entity recognition accuracy in early-stage model evaluation

Web3 data is evolving rapidly, and training AI to understand it requires a partner who’s adaptable, accurate, and informed.

At Femote, we bring domain knowledge, annotation expertise, and a commitment to quality that helps AI teams build smarter systems.