Connecting
Web3 with AI Permissionlessly

Embedd provides developers the toolkit to build web3 data pipelines for AI Apps.
Mainnet

Say goodbye to centralized data for AI Apps

Before Embedd, teams had to develop and operate custom data servers to retrieve web3 data for their AI Apps. Custom solution came with a cost of non-uniformative structure and centralization

Our network

All data is stored and processed on open networks with verifiable integrity. Embedd Network makes querying this data fast, reliable, and secure

Our Solution

Web3 data for AI Apps

Embedd network connects web3 with RAGs in order to build next wave of web3 and AI based application leveraging composabiltiy

Decentralization

Embedd networks has decentralized infrastructure of indexers and curators to provide data to AI Apps.

Programmability

Our SDK allows users to create custom fragments of data for indexing. This is to ensure the application has all the data it needs.

Permissionless dataset

Our permissionless dataset model allows our consumers to use open APIs also facilitating indexer and curators with economic incentive.

Our solution

What you can build

So much can be build

People can create next level AI Apps to leverage RAGs and LLMs to scale existing platforms or to create new from scratch. It’s a new frontier and we’re just getting started.

DeFi

DAOs

NFT Finance

SocialFi

Risk mitigation

On-chain Analysis

On a shared neutral foundation

By combining Web3 protocols, developers can build AI Apps with powerful new features for solving the world’s greatest challenges.

Agency

Own your identity, data, and reputation

Reliability

Guaranteed to run forever on reliable public infrastructure

Interoperability

Seamlessly switch between dApps

Money

Programmable money and financial contracts

Security

Safe, secure, and private

Governance

Transparent rules that people have a voice in

Roadmap

1

Start Q1

  • Embedding model integration in indexer node enabling embeddings through vector databases
  • Custom fragments deployment finalization
  • Hosted service testnet launch on ethereum
2

Mid Q1

  • CLI tooling and SDK development support with other languages
  • Embedd explorer V1 launch to aggregate fragments and enable graphQL query support

Frequently asked questions

Embedd network is a decentralized indexing protocol that curates the blockchain data, converts them into embeddings so that AI Apps can use it.
Indexing organizes data for efficient retrieval, significantly speeding up query responses.
A vector database stores and queries vector representations of data for applications like similarity search.
Off-chain data is information stored outside the blockchain, often in traditional databases.
On-chain data is information stored directly on the blockchain, including transaction records and smart contract states.
On-chain data is information stored directly on the blockchain, including transaction records and smart contract states.
A Large Language Model is an AI model trained on extensive text data to understand and generate human language.
Yes $EMB will be the token for the network usage
A technique in AI that combines retrieving relevant information from a knowledge source with generating responses, enhancing the accuracy and relevance of the output.
A structured representation of interconnected entities and their relationships, used to model real-world information and facilitate knowledge-based reasoning.
Embeddings in AI are like turning words or other data into numbers that a computer can understand. These numbers are arranged in a way that things with similar meanings end up close to each other.
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