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Ai meets Blockchain

GraphGrail Ai – is the world's first Artificial Intelligence platform for Blockchain built on top of Natural Language Understanding technology with the DApps marketplace.
Decentralized platform, open to the world
Ethereum for data-science professionals
GraphGrailAi
Platform v1.0
CryptoPythia
service v1.1

GraphGrailAi token contract address: 0xf207ccfe6d1b90b0b7817ce2d5d262af1514e37b

Why GraphGrail is unique?

More...

CryptoPythia service has launched!
$2 M $10 M
Softcap Hardcap
Pre ICO
$200 K
$
Total tokens: 270 000 000
Token price: 1 GAI = $0.1
1 ETH = 8900 GAI Tokens

Test named entity extraction API:

For example: Tim Cook introduces Apple watch in Cupertino, CNN reports

Latest news

11.04.2018

Announcing Data-label blockchain application

Try our blockchain (currently Ethereum Rinkeby test network) data enrichment application with task distribution over Telegram. Everyone can load csv data and create a task to platform participants work. Result: participants get paid, business get data enriched, ready for Ai neural network training labelapp.graphgrail.com
20.07.2018

We organized our second well established presentation for institutional business representatives

This is second time we present 2 projects, both - GraphGrailAi Platform and MySmartChat (first Platform resident and business-case) - in connection. The aim is to find better way what Platform does, and clever chat-bot is clear and useful case for this.
25.07.2018

New product positioning on GraphGrailAi Platform: Crypto monitoring ("Crypto Pythia")

GraphGrailAi app that collect data about crypto and ico tokens from Twitter to provide real machine-driven data analytics to our users to help make educated decisions on trades, or invest or not in particular ico. Our Ai engine is able to produce reports like: "token X has 2 partnerships, 4 github updates and 0 scam alerts in 1 month time period" cryptopythia.graphgrail.com
13.02.2018

GraphGrail Ai to Establish Partnership with Russian Universities

The GraphGrail Ai project has begun conducting operations directed at establishing working relations with leading Russian universities. After a series of successful meetings, GraphGrail Ai is entering negotiations with MEPhI (Moscow Engineering Physics Institute), RUSNANO, ROSATOM, SFEDU and other universities.
08.02.2018

What is the GAI token (GraphGrail AI)?

A token is a unit of account in the blockchain network used to represent the digital balance of a certain asset. Accounting for tokens is based on technologies that are available through special applications using electronic signature algorithms.
15.01.2018

Opentalks.ai Conference

Participation in the prominent Opentalks.Ai conference in Moscow at the Mail.ru Group company's head office. The founder negotiated several partnerships, pitched the project to investment funds, and met with top speakers in the field of AI, including David Janov from ABBYY, and K. Vorontsov of Samsung Inc. Russia.

We are about to launch a Blockchain MVP!

Request Ai app Join tech questions chat
WHITEPAPER
WHITEPAPER

What we are doing?

We are building an artificial intelligence ecosystem that allows businesses to implement a continuous innovation strategy, to develop and enter new markets.

Downloads

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Whitepaper

Download

Litepaper

Legal opinion

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Other platforms comparition

Legend:

GG - GraphGrail

MA - MS Azure

IW - IBM Watson

YT - Yandex.Toloka

DA - Dandelion API

- Have

- Don't have

- Need to buy

- Need to pay salary

- Need to order custom solution

Platform

Parameter

 GraphGrailGG MS AzureMA IBM WatsonIW Yandex TolokaYT Dandelion APIDA
Platform easy of use, usability
Text targeting, API provided, crowd labeling data
Features: Ai components and algorithms
No programing: easy to use Ai-designer, drag-n-Drop interface to create your custom solution with components, ready to use algorithms, text classification, NEL
Easy solution integration with your business, full cycle
Full cycle solution, connect with any type of source (Social media, internal database), Pre-configured integration with data templates: chat-bot builder, classification, Import & Export data the way you want (JSON, email, etc.)
Blockchain and token economy to cut the costs
Transparent and honest blockchain-based marketplace datastore, blockchain-based quality checking for crowd data-labeling, automatic rating system for platform participants
Get more, pay less: ready to use workflows
Ready to use semantic categories Need to buy Need to buy Need to buy Need to buy
Typical business workflow automatization Need to pay salary Need to pay salary Need to pay salary Need to pay salary
Easy to customize solution Need to order custom solution Need to order custom solution Need to order custom solution Need to order custom solution
Monetization model From 3000 queries per day, from $30 to $500 per month From 2000 queries per day, up to $750 per month
Your business specific crowd labeling specialist on the platform

Applications
Marketplace

Earn money using the platform. Create and sell your linguistic applications for the profit of both data-science experts and business.

Artificial
Intelligence

Using the GraphGrail AI neural networks can be created and trained for various tasks, including complex classification, using Google Tensorflow and other tools.

Programming
Free

Development of chat bots for business, product and services analytics in media, determination of author by text style, precise recognition of emotions based on expressions - all this is possible without programming!

Data
Annotation

Users from around the world will be able to perform tagging on arrays of text data in their languages and to aid business in the area of their expertise.

Smart
contracts

GraphGrail AI is a "brain" for smart contracts. The platform will help automate the execution of smart contracts, with its cross-blockchain ecosystem, webAPI and external data sources.

Laboratory

At the Artificial Intelligence lab data analysis researchers and experts from all over the world will be able to develop and test new and promising solutions (RnD).

Key problems we solve

Tesla learns from cameras and GPS. In natural language all the data can be labeled by people only

Not enough datasets

The fundamental problem is that almost all the available text data is raw, not enriched with metadata and thus useless for training neural networks and chat bots.

TOO SLOW

To automate a process and resolve an end task, a business might need to spend 5-10 months on performing case studies, data analysis, formalizing the development task, testing and finalizing a solution.

TOO EXPENSIVE

Developers, data-science and NLP specialist cost a lot, and using them demands specific knowledge and the time to recruit and manage a development team – which is most frequently outside the business's scope.

DISPARATE SOLUTIONS

Using an off-the-shelf solution, a business loses the ability to update the neural network with new data or has to resort to using different services, which complicates data processing, integration, software code development and requirements even further.

How it works

Full cycle data analysis solution

GraphGrail Ai provides a single solution for text data analysis. We offer all the necessary modules for data preparation and processing: collection and parsing, cleaning, annotation with the help of Ai-designer to build a language model of the subject area, testing, machine training and tuning neural networks for the task. The ready solution is offered at the Decentralized Marketplace and begins earning money. There is no longer a need for other services and complex integration - all tasks are solved on the GraphGrail Ai platform.

How it is now

High costs with the need to collect, process and analyze data manually.

With GraphGrail Ai

An easy, hassle-free service with self-learning capabilities allowing for continuous innovation.

5 easy steps to get things done

Collecting data

Raw text data (posts)
Data collection: Wikipedia, Social media, internal databases, Steemit, Golos.io

Labeling

Structured data
The GraphGrail Ai-designer is an easy drag-and-drop interface for GraphGrail Ai-designer data annotation though an easy drag-n-drop interface for data labeling.

Neural network

Labeled dataset
Training and deep learning through Python Tensorflow, Theano, Keras, Sci-kit learn Training, and Deep learning through Python Tensorflow, Theano, Keras, Sci-kit learn.

integration

Trained datastore
Custom solution
Your business here: Public API, CSV, web UI, chat bot

Create your language solution with any type of initial data stream

An Ai-designer is like a set of building blocks, because it allows combining several typical NLP modules of the GraphGrail Ai platform in any sequence to create the solution you need without programming!

Ai builder

The Ai-designer is like a set of building blocks, because it allows combining several typical NLP modules of the GraphGrail Ai platform in any sequence to create the solution you need without programming!

  • Various semantic text analysis layers: from morphological and syntax analysis to high-level syntactic and pragmatic analysis;
  • Flexible configuration of logic and workflow in streams of text data and sequence of algorithm application;
  • The possibility of creating Agents - independent application components interacting with each other to solve complex artificial intelligence tasks;
  • Setting pipelines during application of algorithms and parallel use of trained neural networks (RNN, LSTM).

Data Markup Interface

STEP INTERFACE (APPLICABLE in the case of COMPLEX CATEGORIZATION)
ONE TIME QUESTION?

Is there a bathrobe for a nurse with protection from various liquids that is inexpensive? OBJECTIONS?
yes No

Benefits

ANY NUMBER OF CATEGORIES

Use categories flexibly, with the possibility of nesting categories in lists or trees of ontologies depending on the complexity and detail of your business objects.

ANY COMPLEXITY

Thanks to our sets of pre-trained models, the system is able to parse and prepare data from internal databases of business customers, thus reducing manual work.

ONTOLOGIES and RELATIONS

The system includes preinstalled lexico-semantic features including synonyms, antonyms, category-subcategory, part-whole relations. Handling complex semantic categories is now made simple.

Cases

Monitoring (social media) analyzing and finding problems with products and services of businesses, monitoring publications of competitors about cryptocurrencies, analyzing the media field in politics, and streaming news.
Technologies: Aspect sentiment, Named Entity Recognition, Business-specific ontology, Multi-class classification, CNN.
The contracts ((Ethereum, EOS, Bitshares)) can use Oracles to interact with the out-of-blockchain world. This creates obvious trust issues, for instance proving that the conditions of a smart contract have been fulfilled in the real world. A smart contract can easily check a bank account, but the conditions in the real world, such as weather, change of company ownership, political or legal decisions, details of contracts and force majeure circumstances are much more difficult to check. GraphGrail AI solves these tasks.

Tokens economic

Token distribution scheme and project costs

Token role

The GraphGrail Ai GAI Token is utility token and acts as internal currency in the system. The Tokens provide NLP end product businesses customers access to the system and the ability to quickly order and receive a solution, such as software development, or an application and data markup for it.

Tokens are paid to data markers for their work. Tokens are also paid to the testers and voters for the model - delegates, who monitor their quality and community. The balance of demand and supply of tokens on the platform is achieved due to flexible pricing - the payment to the participants of the platform is greater the more difficult the work on the markup of data.

Advantages for Token holders

The ecosystem is maintained by the supply/demand balance of services and orders from business clients. The more clients, the more people working on data annotation, the greater the scope of segments, each of which requires custom language models. To access the platform, a business representative must buy from 5,000 to 10,000 Tokens on the exchange. Thus, liquidity is withdrawn from free circulation. A business can spend the Tokens on purchasing internal services on the platform, such as data collection, cleanup, tagging, custom settings for training neural networks, etc. The more participants in the system and the more orders are placed in the marketplace application, the greater is the platform's benefit for business, providing long-term capital through the accumulation of valuable data and models for all parties, such as data taggers, businesses, and model merchants in the marketplace.

Roadmap

2014

July - establishment of the company
First sales of language analysis analytic solutions to business clients
Search service, based on own concept, developed. Successfully sold

2015

Incubation at the South IT Park
Receiving first investments

2016

Sales of analytical studies to business customers: marketing agency, LinguaLeo
GraphGrailAi is one of three companies in Russia providing a search and fine semantic analysis service of statements in the Internet

October 2017

GraphGrail Ai presale.

February, 2018

Use-case release on the GraphGrailAi platform - a monitoring system with flexibly tuned settings to categorize texts using language models

March - May, 2018

Use-case release on the GraphGrailAi platform - smart chat bot for sales automation
Release of a Data tagging component that can be commissioned by third-party business customers

June - July, 2018

Full-scale platform launch, linguistic models designer and neural networks with access via application programming interfaces (APIs).
Testing and connection of the 2nd language for platform - English

August - October, 2018

Release of language models marketplace component with monetization and payments for API queries.
Implementation of ready-to-use semantic categories sets (category-subcategory, taxonomy, part-whole)
Implementation of blockchain for data markup quality assessment (proof-of-quality-work)

First quarter 2019

Creating a Deep machine learning and future artificial intelligence prospective solutions laboratory based on the platform models (RnD Laboratory with deep learning)

Mas Media about us

Криптовалюты.рф

GraphGrail Ai blockchain project experience: how to raise $ 200000 in 15 hours?

Firrma.ru

GraphGrail Ai announced raising more than $200 thousand in two weeks of pre-ICO Golos.io

Golos.io

Official blog of the GraphGrail Ai platform on the blockchain-media platform Golos.

Team

Our team consists of highly experienced professionals in various areas, covering every required competency.
The team includes specialists with over 6 years experience in data science and natural language processing.
We have successfully completed several projects for businesses and the government in the past. Most of the team has solid experience in research and University projects, as well as sales and marketing.

Victor Nosko

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

Alexander Borodich

Venture investor, Advisor.
Universa Blockchain founder ($28mln on ICO).
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

Zakhar Ponimash

Consultant in neural networks.
Engaged in neural networks and strong AI.
Game developer, based on XNA framework, TCP/ip chat, chat bots, text understanding systems. 2 times was the first at the university conference on economics, 2 times at radio engineering. Once was the 1st in the IT park hackathon. Participated in the IT park acceleration program with “patterns recognition" project. Good knowledge of C#.

Maria Tarasova

Journalist
Candidate of philosophical sciences, specializes in simulation modeling, data mining and statistical data analysis. Was awarded a scholarship of the President of the Russian Federation and the Government of the Russian Federation for her high contribution to science, author of more than 60 scientific research works on the social processes modeling, an active participant in five grants of the Russian Fundamental Research Fund and participant of more than 10 Russian national and international conferences.

Frequent questions (FAQ)

What is artificial intelligence?

Artificial General intelligence (AGI) is a system with the cognitive abilities of a human, capable of performing a wide range of tasks and to apply this knowledge to solving unfamiliar problems without preparation.

What is an artificial neural?

Artificial neural network (ANN) is a mathematical model, as well as its software or hardware implementation, based on the principle of organization and functioning of biological neural networks — networks of nervous cells of a living organism.

What is сomputational linguistics?

Computational linguistics (also: mathematical or computational linguistics) is a research area in the field of mathematical and computer modeling of intellectual processes in humans and animals for creation of artificial intelligence systems, which aims to use mathematical models to describe natural languages.
Computational linguistics partially overlaps with natural language processing. However, in the latter the emphasis is not on abstract models, but rather on applied methods of describing and processing language for computer systems. The field of activity of computational linguists is developing algorithms and applied programs for processing language information.

What is an annotation?

(Linguistic) annotation (tagging) is the process or result of assigning special labels to texts and their components. Linguistic annotation is one of the basic concepts of corpus linguistics. Annotation enables identifying texts according to different parameters, allowing carrying out a meaningful search in the corpus. Linguistic annotation as such is divided into: morphological (separation of affixes, compound words, etc.), lemmatization (specifying the original from for each word of the text), etc.