FAQ

FAQ

General

Quoth is an all-chain protocol to search, authenticate and bridge NFTs by using AI and Machine Learning.

  • Real-time rarities: Collections and collectors will not have to wait long periods to check the rarity of an NFT; normally, this takes about 1 week from the minting time as it has to go through an application process. On Quoth, collections and their rarities will be listed within 1-2 hours of going live. 
  • ALL NFTs in one spot: Our AI is indexing all NFTs out there, from all chains. At the moment we have indexed about 90% of all Ethereum NFTs.
  • Metadata tracked dynamically and free of cost for collections: This is especially important for projects as anytime the dynamic rarity changes, they will not have to update it nor pay for it. It will happen automatically on our platform.
  • Google-like search of all NFTs: We are creating a google-like semantic search, where you can type in any words and you can find whatever NFT will be indexed from those words.
  • Partnership with top NFT collections: Quoth has strategic partnerships with several NFT collections, among them SupDucks, Untamed Elephants, Based Fish Mafia, and others. Collections partnering with Quoth will be able to train the neural net with the help of their communities, this will be rewarded with a Quoth NFT.

Artists, collectors, buyers, traders, marketplaces, and wallets.

  • Wallets and marketplaces verifying NFTs to ensure authenticity and originality.
  • Collectors get true market rates of NFTs rather than floor prices.
  • Marketplaces authenticating NFT projects for pre listing to ensure authenticity.
  • Collectors bridging assets to all chains to gain access to deeper liquidity and yields using popular defi protocols.
  • Platforms securely bridging NFT assets to other networks to access a wider range of collectors.
  • Owners looking to maximize returns by fragmenting the original NFT into NFTs on multiple chains.
  • Future use case as an NFT licensing mechanism replacing traditional copyright with a crypto native solution.
  • AI recommendation engine for NFT appraisals and similar for sale options algo.

The training of our Beta MVP will be available only through whitelist. To be whitelisted you need to be a holder of one of the NFT collections in partnership with us.

Yes. The top participants that have the most active and accurate search and click results that help our ML train the neural nets will be auto minted an NFT that:

  • Is tradable
  • Grants all-access features only these few selected participants receive
  • Forever all access to all future releases
  • Exclusive direct to the devs discord channel
  • Elite status among Discord members — forever!
  • Free advertising on banner ads

Please reach out via Twitter DM or use this form.

Utility token on Binance Smart Chain and Ethereum.

The token will be based on the Binance Smartchain for the IDO, and later it will be based on a ERC-721 contract (ERC-721 is a standard that is compatible with BEP standards, the basis is the implementation of ERC-721, which in essence is not different from the BEP).

The contract on the Binance network will be expanded with additional functionality to enable the liquidation of the token when its counterpart is released on the Ethereum side.

Our native token, $QUOTH, is listed on multiple platforms such as MEXC and PancakeSwap.

There is a total supply of 55,000,000 tokens.

Token DistributionNo. of TokenPercentage(%)Offering Price (USD)Value (USD)
Seed2,750,0005%0.30$825,000
Private5,716,66710.4%0.45$2,572,500
IDO883,3331.6%0.60$530,000
Team8,800,00016.0%  
Advisors3,300,0006.0%  
Marketing4,950,0009.0%  
Liquidity5,500,00010.0%  
Community Incentives9,350,00017.0%  
Strategic Reserves13,750,00025.0%  
Total:55,000,000100%  

No. The supply is fixed, there isn’t any type of burn/tax on them.

FAQ

Quoth Token

FAQ

Tech

It will identify NFTs on blockchains and pull the following data:

  • Contract addresses
  • All associated transactions, timestamps, price changes and originator information
  • Metadata
  • Links to the data, contained in the metadata

The data then will be processed and tagged and processed by the AI, identifying the type of data (image, video, audio, text etc.), performing semantic image description, image-to-text, sound-to-text, video-to-text services and creating a database, ready for reverse image, video and audio searches.

  • The authentication process works in 3 steps:

    Submit an image or token address.
    Our system will show you similar NFTs. You will be able to see the minting date/timestamp of the results.
    A similarity percentage will be generated, showing you how original your NFT is

The originality detection component will perform two kinds of checks: 

  • Literal compare: Two quotes will be regarded identical if they have coinciding punctuation and spelling errors. Common spelling errors will be corrected automatically using ML models trained on existing quotes and NFTs (their names and descriptions). 
  • Paraphrase detection: Two quotes will be regarded identical if they have similar semantic representation, up to the threshold. The threshold and exact type of semantic representation type will be optimized during the project implementation.
  • Self-supervised training of custom language model (word embedding); in case of small datasets (less than few gigabytes of text), some suitable large text dataset, such as Wikipedia, is added to the target dataset. 
  • Based on the language mode, semantic representation of each document in the dataset is calculated. 
  • Several clusters are found in the semantic space of the dataset to find distinct ‘islands of meaning.’
  • For the visual inspection of the dataset, a two-dimensional map of the document collection is calculated; this map connects the topics, keywords, and documents. Note: in the two-dimensional representation the topics only approximately correspond to visual clusters (‘blobs of meaning’). 
  • Optionally meaningful names can be manually inferred for the found clusters. 
  • Finally, for longer documents, the last 4 steps are first performed for short (10-200 words) excerpts from original documents and then repeated to produce ‘document classes.’ 
  • This approach enables to tag the document collection with minimal human labeling. The resulting topics (usually, from several tens to several hundreds of topics are found) are far easier to label than manual labeling the initial document collection. 
  • In addition, the visual representation of text collection provides an overview of the text collection, relations between groups of text and helps to select the most important tags (document categories). 
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