Santiment is building a decentralized sentiment information market for blockchain assets. It combines machine learning, reputation systems, and sentiment analysis to better understand blockchain market trends. Santiment's goal is to allow users to buy and sell information related to crypto-crowd emotions, and to provide more access to financial data related to blockchain assets. The network will be powered by the SAN token.

Safex is developing a decentralized marketplace with an emphasis on privacy, anonymity, and a secure listings. The Safex ecosystem is powered by the SAFEX coin, which has a Proof-of-Work (PoW) consensus mechanism. The decentralized marketplace will require all listings to be curated by a centrally managed system that will be based on participants staking each listing.

CyberVein is a distributed data network that enables the decentralized management of complex datasets on the blockchain itself. CyberVein's goal is to become a public network of immutable databases, on which information can easily be traded, interconnected, and transformed into structured knowledge.
CyberVein allows multiple parties to process distributed databases that are shared and immutable. CyberVein's databases can be connected, referenced, and moentized. This creates a marketplace for data that can benefit researchers, businesses, and others.
CyberVein's directed acylic graph (DAG) architecture improves upon existing blockchain networks, and enables a larger amount of data to be stored and transmitted by multiple parties. CyberVein has an innovative consensus algorithm called Proof of Contribution (PoC), which gives incentives to nodes to contribute storage resources. Their ledger system consists of a network of interconnected smart contracts that are designed to function independently, but can easily communicate and exchange information between the. CyberVein also uses a data oriented contracting language is a modified version of Solidity that is optimized for data monetization.
CyberVein's innovative twist on data management networks have the potential to be very useful in cases were datasets need to be shared or when data needs to be processed by several parties in parallel. Some examples include machine learning, IoT systems, and cross-industry research.