GraphAI is making a billion dollar impact to Web3 as the first native AI data layer, delivering real-time, dynamic knowledge graphs and memory extension for large language models and agents. This breakthrough infrastructure puts GraphAI in a league of its own—positioning it as the go-to platform for building the next generation of autonomous DeFi protocols, real-world asset integration, and intelligent on-chain applications.

Before GraphAI, Akhil and Mridul were building the infrastructure behind the world’s most advanced AI at Google. Akhil engineered multilingual NLP and classification systems powering digital assistants and search for billions. Mridul, meanwhile, led large-scale recommender and generative AI projects across Google’s platforms, creating models that deliver real-time, personalized experiences to users everywhere. These two engineered how AI scaled at Google in their respective fields, transforming product lines, touching millions, and setting global benchmarks for reliability and intelligence.

Now, they’re bringing that same ambition and engineering muscle to Web3.

Web2’s smartest AI platforms never think alone. They lean on titanic knowledge graphs, living, synaptic networks that update faster than you can say “zero-shot.” Web3, meanwhile, still asks each project to build its own plumbing. Data from blockchains here, real-world asset feeds there, and a thousand lines of bespoke ETL glue in between. The result? AI products that look clever in a demo but run out of context the moment money is on the line. GraphAI exists to change that equation turning context into a public good, not a private headache.

Fragmentation is rife with on-chain events, off-chain attestations, and RWA price ticks live in separate universes. Teams repeatedly reinvent the same data pipeline and when models can’t see the full picture, they hedge, hallucinate, or simply freeze none of which pays the bills.

GraphAI weaves its retrieval-augmented engine, GraphRAG, into a Model Context Protocol (MCP). In practice, this means dynamic sub-indexing where any transaction is promoted to a first-class citizen in a knowledge graph. Every fact arrives with its own cryptographic receipt, so auditors can sleep. Data is delivered in the exact shape large language models prefer, sparing you prompt acrobatics, and one-call composability lets you pull context once and reuse it in risk engines, treasury bots, or customer dashboards. A game changer that any dApp can tap into, now, every developer has access to instant AI hosted data to extend Web3 Use Cases.

DeFi desks, RWA platforms, and analytics teams have all reached the same conclusion: there is no competition. Early partners are already integrating GraphAI to power agents that trade, hedge, and report in real time, the sort of functionality that used to require a custom data science team in a glass tower.

Index-to-Earn, run a node, serve sub-graphs, collect usage fees. Query-to-Burn, every request retires a sliver of supply; demand does the tightening for you. Incentives that nudge the right behaviour.

Akhil and Mridul’s innovations at Google set a new bar for AI scale and performance. Now, with GraphAI, they’re bringing that same level of clarity, context, and power to the next generation of Web3 intelligence.

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