Key takeaways: * APIs are simple and fast, but limited to predefined queries * SQL enables flexible, composable queries that align with how AI systems operate * Blockchain data is difficult to serve via APIs without heavy normalization and standardization across chains and protocols * Most meaningful blockchain metrics (balances, positions, flows) must be
Key takeaways: * More crypto analytics and investigations are being assisted by AI * AI systems can use MCP to turn prompts into defined operations * AI can still hallucinate with MCP, risks are still involved * The quality of the data layer will determine the quality of the crypto investigation or analysis 💡What
Key takeaways: * No crypto systems should rely on infrastructure that creates a single point of failure * But data accuracy matters just as much — blockchain data needs to be a system of record for projects to be fully protected * Verification didn’t fail in isolation. The issue was the state it
Key takeaways * Blockchains don’t provide data in the right format to easily build crypto wallets * Many blockchain data platforms have siloed data tools, complicating the wallet building process * PnL is the most difficult problem to solve when building a crypto wallet * Allium provides one platform that simplifies the wallet
Key takeaways: * Crypto wallets used to only need to send, receive, and show balances * The crypto industry’s growth means that users require more from their wallets — like portfolio views, trading capabilities, staking, strategic AI assistance * Traditional wallet infrastructure wasn’t built to support this level of execution * Allium’s
Key Takeaways: * Blockchains store data, but aren’t built for querying * Indexing turns raw data into structured, usable datasets * Without indexing, analytics doesn’t scale * Modern stacks separate ingestion, indexing, and querying * Batch = historical accuracy, streaming = real-time data * AI systems rely on indexed, model-ready data Blockchain indexing is
Key Takeaways: * Raw blockchain data can’t be used for high-level analysis * Onchain data needs interpretation * Data normalization is necessary to understanding onchain activity * Onchain data normalization is foundational infrastructure in every blockchain data stack * LLMs and AI systems require normalized data 💡What is onchain data normalization? Onchain data
Key takeaways: * Blockchain data is not as straightforward as its transparent nature makes it seem * Different blockchains define data differently * Small differences in data interpretation can snowball into large inconsistencies * Blockchain isn’t yet a system of record — but it can be with the right crypto data infrastructure 💡What is
Most teams working with blockchain data eventually discover the same thing: analytics doesn’t come from a single tool, it comes from a stack. A blockchain analytics stack is a pipeline that transforms raw blockchain data into structured, queryable datasets. It includes layers for data ingestion, decoding, indexing, storage, and
TL;DR * Agent economies are already emerging * Identity (ERC-8004) and payments (x402) solve trust and transactions * But agents still lack reliable onchain data * Without structured data, agents make silent, compounding errors * Onchain data is the limiting factor for scaling autonomous systems in crypto 💡What is the agent economy? The
💡Institutional-grade blockchain data refers to onchain data that is structured, auditable, and reproducible, enabling its use in financial reporting, compliance, trading, and risk management. TL;DR Institutional-grade onchain data must be: * Auditable (full data lineage) * Standardized (consistent schemas across chains) * Complete (full historical coverage) * Traceable (across entities and
Institutional buyers now expect SOC 2 from blockchain data vendors. Here is what the framework covers, how providers achieve it, and who is compliant in 2026.