Ormi is the entity and reputation discovery layer for Web3. Ormi provides Web3 protocols/projects with reputation, risk, and entity insights on any address.
Below are the three verticals that Ormi is focusing on:
Sybil accounts and bots are having an increasingly negative impact on Web3 protocols, ranging from token airdrop to Web3 games to Quadratic Voting/Funding; it's giving users with fake accounts an unfair advantage over real human users and negatively affecting user retention. To combat this issue, Ormi has developed a system to accurately detect and map multiple seemingly unrelated addresses to the real entity behind those addresses. By doing so, protocols can implement clearer boundaries that only allows one single entity eligibility for rewards or participation, ensuring that the real user has a fair chance against those using false identities.
Ormi enables users to gain a deeper understanding of the activities and behaviors associated with an address beyond the hexadecimals and token transfers of a pseudo-annonymous address. Leveraging Web3 data (blockchain raw transactions, Web3 social graph, IPFS), Ormi builds a multidimensional knowledge graph and utilizes its internal labelling algorithm and machine learning model to generate descriptive labels for addresses, enabling both analytics and machine learning training purposes.
Utilizing our comprehensive Web3 knowledge graph and entity and reputation computation models, we offer tailored trust and reputation scores to minimize counter-party risks associated with your Web3 activities. Additionally, we label entities connected with stolen funds, hacks, vulnerability exploits, rug pulls, and scams and related addresses, allowing you to eliminate counter-party and regulatory risks associated with suspicious, hacker, or sanctioned addresses.