GraphGrail Ai – is the world’s first Artificial
Intelligence platform for Blockchain built on top of Natural Language
Understanding technology with the DApps marketplace
Fill the form - main Crowdsale (ICO) (cap is $2M-12M)
Whether you are a data scientist or businessman, you can make your own linguistic applications, sell them and make money.
With GraphGrail's AI platform, by using Google Tensorflow and other tools, you can create and teach neural networks for complex classifications.
Key problems we solve
Tesla learns from cameras and GPS. In natural language all the data can be labeled by people only
What is the key problem in data-science and text mining? There simply aren't enough datasets with seven figure sample sizes (from thousand to million samples) to teach neural networks in many industries, including banking, telecom, media and government agencies. Even if the business does have such datasets, using machine learning to gather, clean and make training and test sets is extremely difficult and time consuming even for skilled data scientists. Training such as this can take between 5 and 10 months for a normal sized dataset, and even though we use neural networks and Deep Learning frameworks to simplify the task, it is a one time solution - every step must be repeated over and over again to keep your linguistic models up to date. That's why business truly needs GraphGrail AI.
Smart contracts are not so "smart" as they could be
Smart contracts, by their nature, are able to run algorithmic calculations and store and retrieve data. Oracles fill this void by watching the blockchain (Ethereum, EOS, Bitshares) for events and responding to them by publishing the results of a query back to the contract. In this way, contracts can interact with the off-chain world. This introduces obvious trust problems. Another complex problem is to prove that smart contract conditions are executed in real world. Smart contracts can easily check the amount of a bank account, but real-world conditions like weather changes, companies' buy-sell events, political or legal decisions, contract details and force majeur events are much more complex. This is where GraphGrail AI comes in. Read more
How it works
Full stack data analysis solution
GraphGrail AI is an all in one, full cycle solution. It offers all necessary data preparation tasks, including text parsing, cleaning, our AI designer for building linguistic models, testing facilities, machine learning and neural network tuning, and a decentralized app marketplace to make money with the platform. You will not need any other service.
High costs. You must gather, process and analyze the data by hand.
With GraphGrail Ai
An easy, hassle-free service with self-learning capabilities: continuous innovation.
With GraphGrail AI, you can lower risks of implementing smart contacts in you business. Our technology offers convenient stack, including a data oracle, a trust gate to real-world data including social networks (Steemit, Golos.io, etc.), Wikipedia, and news resources. The smart contract can check conditions automatically, detecting weather changes, force-majeur circumstances or political risks, adding real agility to the smart contracts industry.
Big risks if something goes wrong: for example, the business may suffer from a legal issue and cannot execute an agreement upon smart contract - even though the contract is the code.
With GraphGrail Ai
You can avoid the risk: our service can prove that some valuable data in real world has changed, letting you either make a new contract or exploit the existing one in your favor.
5 easy steps to get things done
Create custom solution from any data flow
Ai-designer is helping you. Combine any "black box" NLP modules, provided by GraphGrail Ai to make your custom Workflow. No programming required
Token distribution and costs.
Role of the Token
GraphGrail Ai (GAI) token is a utility token that acts in the system as an internal currency. For a token, a business that orders NLP end product gets access to the system and the opportunity to quickly order and get the solution – a software development of the application and data labeling for it.
Tokens are paid to data scientists for their work. Tokens are also paid to testers and those, who votes for the models – delegates that supervise their quality and the community. Balance of token supply and demand in the platform is achieved through flexible pricing – the more complicated is data labeling, the higher are payments to platform users.
Advantages for investors
Token price grows because the ecosystem grows and evolves – more clients, more people involved in data labeling.
To access the platform, a business representative has to buy 5,000 – 10,000 tokens in the stock exchange. Thus, liquidity is withdrawn from turnover, and the cost of token increases.
Business will be able to spend these tokens on purchasing internal services of the platform: data gathering, cleaning, labeling, custom settings to train neural networks etc. The more participants there in the system, and the more orders are placed in the application marketplace, the higher is the token cost and economic value.
GraphGrail AI's team has more than 6 years of experience in the fields of data science and natural language processing tasks. We have successfully completed several government and business projects. Most of the team have solid experience in science and university projects.
CEO and founder, Ai, Data-science.
Python developer, Django framework. Data-science specialist, NLP stack: NLTK + Celery + Pymorphy2 + GLRparser etc. Victor has more than 6 years of experience in development and deep learning. Experienced in Google TensorFlow
Python developer, Django framework. Sergey has more than 5 years of experience in development, science projects, system analysis, and databases
Venture investor, CMO.
Futurologist, angel investor, serial entrepreneur, founder of VentureClub, MyWishBoard, MyDreamBoard, and SuperFolder. Chief Dreams Officer and partner in Future Action, founder of crowd-investing platform VentureClub.ru. Alexander has solid business experience
Ai, Data-science, Developer
Strong AI developer, C#. Data-science specialist. Zackhar has more than 5 years of experience in signals processing, neural networks, deep learning, and chat bot development
Blockchain, Fullstack Developer.