We’re excited to share a major milestone in the development of Grey Hat Labs’ AI Schema Learning Framework (ASLF) — the first working demo of our schema ingestion and normalization engine is now live and functional.
This internal prototype showcases the ASLF’s ability to automatically recognize, standardize, and normalize blockchain transaction schemas across different data sources. Built as part of the core Schema Learning & Reinforcement Engine, the demo proves out the foundation of our AI-driven interoperability layer.
🔧 What We Built
• A modular Express + TypeScript service (schema_engine) capable of ingesting and normalizing transaction data.
• A canonical field mapper (v0) that aligns incoming data into standardized schema structures.
• A demo interface — a lightweight static HTML frontend — that lets us submit test transactions, visualize normalized output, and validate confidence metrics.
• Full CI/CD integration, ensuring automated testing, image builds, and ECR publishing for every new commit.
🧠 Why It Matters
This demo is an early step toward an adaptive schema learning system — one that can:
• Automatically infer and evolve schema mappings,
• Reinforce itself based on real-world feedback, and
• Power seamless multi-chain interoperability within the GHL Platform.
🔜 What’s Next
Over the coming sprint, we’ll connect this schema engine to our RLIE (Reinforcement Learning Inference Engine), introducing live feedback loops and performance telemetry to improve normalization accuracy.
Stay tuned — this is where AI starts to learn how blockchains talk to each other.
