The first adaptive, self-organizing ‘smart ledger’. Fetch is a next-generation protocol built with a ready-to-go Useful Proof of Work system invented by AI experts.

Rating: 3.8

Official contacts
Location: N/A
Web-resources:   Whitepaper Website
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Details Description Risks Full analysis Team

Detailed review

Stage of product development
The team has a detailed road-map and development schedule that goes into 2019 and beyond. Briefly, though, the team is looking at a public test-net launch in the summer of 2018 with new functionality being added as the year rolls on. The initial main network release will be in the first half of 2019. A more detailed roadmap will be released early summer.

Token description
ICO date
25.02.2019 - 25.02.2019
Token price
Hard cap
Fetch Smart Ledger
Token distribution date
Consensus method
Useful proof of work (uPoW)
ICO currencies
Bounty camping
Token functions
Tokens are used on the network as the digital currency for all transactions, as well as for network operations such as secure communications. Tokens can also serve as a refundable deposit for both nodes and agents wishing to perform certain operations (as a security mechanism that discourages bad behaviour). An initial utility token will be issued on Ethereum for the token sale.

Fantom is not the first platform aiming to simultaneously process multiple blocks. IOTA, Byteball, Nano and Hedera Hashgraph already started developing projects with somewhat similar technology.


There is no link to GIthub or other source to track the product development Currently, beta version of the platform is not available for the public A detailed roadmap has not yet been published.


The exact "gas" prices have not been published yet and subject to change.


Now team does not have marketing specialists with proven track-record.

Full analysis

Blockchain technology has provided a way to maintain consensus across all nodes with no central authority. However the technology faces fundamental issues like a lack of real-time transaction settlement and scalability. Despite improved consensus algorithms, current blockchain implementations use nodes that synchronize one block at a time. This results in slow confirmation times, one of the biggest factors stopping blockchain technology from being widely used across many industries. Although third-generation Smart Contract platforms such as Cardano and EOS have emerged, scalability issues have not been resolved. To address these persistent issues, a new model based on the Directed Acyclic Graph (DAG) such as IOTA, Byteball, Nano, and Hashgraph improve on current blockchain scalability. This new technology does guarantee real-time transactions because nodes process transactions asynchronously, while ensuring infinite scalability and potentially achieving hundreds of thousands of transactions per second as more nodes participate in the network. However, these platforms lack the Smart Contract dApp infrastructure provided by platforms like Ethereum, as well as stability.


Fetch AI was founded in early 2017, following a merger of two independent companies, Itzme AI and uVue. Itzme AI was operating in the field of social networks while uVue was involved in developing control systems for autonomous vehicles. Beta not available for the public. Public test-net launch scheduled in the summer of 2018. A detailed roadmap has not yet been published and will be released early summer. There is no link to GIthub or other source to track the product development Fetch's system will consist of three layers. Layer 1 is the autonomous economic agents, AEAs, which live in the environment provided by layer 2, the OEF. Third parties will be able to implement the agents onto Layer 1, using examples, generic agents and toolkits for development, provided by Fetch. Underpinning the OEF is the ledger that ensures the integrity of the global truth on the decentralized network and feeds the learning that provides trust, reputation and network intelligence. Layer 2 and 3 form a node. Fetch's peer-to-peer network is made up of many such nodes connected to each other in different ways. These nodes include source code and ready-to-run deployments maintained and provided by Fetch. Machine learning and intelligence is supported at all three levels.

Business model

The OEF's primary API, as exposed to agents, supports a number of base-level commands. Some of these commands are free, others require a small token cost. Other commands require what we term a "trolley token" — a small deposit in Fetch tokens that is refunded if the operation is gracefully completed. Operation costs are decoupled from the Fetch token in a similar way to that of "gas" in the ethereum network. Thus there is a need to convert Fetch tokens to an operational fuel before commencing an operation. The node that performs the conversion receives the Fetch tokens in exchange for providing that service. The reward for providing these commands to AEAs is given to the node's operator. There is a constantly fluctuating conversion rate from the Fetch token to the Fetch operation fuel. Fetch operation fuel is used to pay the execution fee for operations on the Fetch network. Trolley tokens are managed in a smart contract on the system. These are refundable Fetch tokens that are required for some operations. The token is automatically refunded when the operation is complete or when the other party involved fails ungracefully. There will be lock up schemes for the core team and early investors to ensure long-term incentives in the project.




Now there are 15 people working at Fetch.ai. Most of the core competencies are covered by the team members. Currently, the team only lacks marketing specialists. The core team consists of AI & ML experts with proven track record in these fields and strong track record. The empoyees includes people experienced working at DeepMind (acquired by Google in 2014), Bloomberg LP, Nokia and several succesfull startups. Some of them have advance degrees in IT fields from Cambridge, ETH, TU Vienna, UCL, and University of Manchester. The CEO of Fetch.ai has tech & entrepreneurial background, as well as other co-founders.

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