Data-driven research, guides, and insights for leaders building the infrastructure and intelligence to bring institutions on chain, by the data plumbers at Allium.
Insights
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
Research
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
Guides
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
Insights
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
Guides
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
Guides
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
Insights
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
Guides
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
Guides
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
Guides
💡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
Insights
Blockchain data infrastructure is increasingly used by institutions, not just crypto native teams. Banks, fintech companies, trading firms and compliance platforms now rely on onchain data to power production systems, risk monitoring, analytics and payment infrastructure. As this shift occurs, the expectations placed on blockchain data providers are changing. Institutional
Customers & Partners
As blockchain networks evolve into financial infrastructure, access to reliable, normalized onchain data becomes essential. For ecosystems targeting institutional adoption, the ability to measure activity, track capital flows, and identify key participants is critical to operating and competing at scale. Aptos, a production-grade Layer 1 built for performance and