Elfa AI

We give agents and humans real-time awareness of the world. Stop scrolling. Set conditions. Get triggered. Act.

Iris is a real-time intelligence stack from Elfa AI that gives AI trading agents live context on financial markets, starting with crypto. It combines social chatter, on-chain activity, and market data into a single signal layer that agents can query or subscribe to, so they can act on sentiment shifts, narrative moves, and early insights the moment they form rather than after the fact. The launch matters because Elfa AI is rolling out a platform that lets AI agents listen continuously to market conditions and execute transactions on Solana in real time, triggering automated actions the moment specified conditions are met , which pushes agent tooling past static prompts into event-driven execution. Iris ships alongside Alpha, a consumer-facing agent that sits on top of the stack. Traders can ask Alpha open questions about what is happening in a token or narrative right now, or hand it a set of conditions and let it watch, flag, and trade on signals in the background. Developers get the same primitives through an API, so any agent framework can plug in for surveillance, entity linking (who is involved, how they are connected, what it led to), and execution triggers without building a data pipeline from scratch. Elfa indexes price and trading-related data, social chatter, news, and prediction markets continuously, and surfaces structured insights the moment something relevant happens. The company is headquartered in Singapore and building a real-time context layer for agents , led by co-founder and CEO Tristan Teo. It has already been embedded across the crypto data stack, powering a share of CoinGecko's altcoin and macro insights and integrating with builders on Virtuals and Drift, and its backers include Amber, along with angels such as CoinGecko co-founder Bobby Ong and Etherscan founder Matthew Tan. Iris is the productized version of that infrastructure, aimed at teams who want their agents to trade on current reality instead of stale training data.

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