DeagentAI operates a decentralized artificial intelligence agent network that utilizes a "Proof-of-Insight" protocol to integrate user feedback into the AI training process. This approach enhances model accuracy and reliability, specifically targeting inefficiencies in trading across primary and secondary markets.
Funding
$6M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Inefficiencies in trading across primary and secondary markets in Web3 persist due to a lack of accurate and reliable AI models that can effectively incorporate user feedback into their training process. Existing AI solutions often struggle to adapt to the dynamic nature of Web3 markets, leading to suboptimal trading outcomes.
Solution
DeagentAI is developing a decentralized AI agent network that uses a "Proof-of-Insight" protocol to integrate user feedback directly into the AI training loop. This mechanism aims to enhance the accuracy and reliability of AI models used for trading in Web3 environments. By tightly coupling user feedback with the training process, DeagentAI seeks to address the specific challenges and pain points present in both primary and secondary markets, ultimately improving trading performance. The initial product, AlphaX, is designed as a Web3 agent trained using this feedback mechanism.
Target Audience
The primary target audience includes traders, investors, and other participants in Web3 primary and secondary markets who seek to improve their trading performance through the use of more accurate and reliable AI-driven tools.
Features
- "Proof-of-Insight" protocol for integrating user feedback into AI model training
- Decentralized AI agent network architecture
- AI models specifically designed for trading in primary and secondary Web3 markets
- AlphaX: The first Web3 agent utilizing the "Proof-of-Insight" feedback training mechanism