Building Personalized Web3 Experiences with ❜embed’s AI Recommendation Platform on AWS

This article describes how ❜embed built a scalable AI recommendation engine on AWS to personalize Web3 application experiences using onchain behavioral data.

• Web3 platforms lack personalized recommendations despite rich onchain behavioral data availability
• Challenge: High-volume, high-velocity onchain data across multiple blockchains and protocols
• Challenge: Complex ML operations required for diverse Web3 user behaviors and temporal patterns
• Challenge: Specialized expertise needed in blockchain, data engineering, and AI/ML
• Layer 1: Amazon Kinesis ingests onchain data; Lambda normalizes; custom embeddings created via LRM
• Layer 2: Pinecone vector database for similarity search; Aurora Serverless for relational queries; ElastiCache for sub-millisecond feature retrieval
• Layer 3: SageMaker trains Web3-specific models on 100M+ onchain content pieces
• Results: 2-3x engagement uplift; 5-6x increase in prediction market recommendations; 50% of trades from AI feed

❜embed democratizes Web3 recommendation capabilities through AWS managed services, enabling rapid deployment without requiring deep internal AI expertise.