ICOrating

GRAPHGRAIL AI Rating Review (http://graphgrail.com)

ICO dates (19.02.2018 — 15.04.2018)

Web: icorating.com

Email: info@icorating.com

Twitter: @IcoRating

1. Ratings 3

2. General information about the Project and ICO 4

3. Description of the services and scope of the project 6

4. Market Review 9

4.1. Market analysis 9

4.2. Competitors 10

5. Team and stakeholders 13

6. Token analysis 15

7. Analysis of factors affecting the future value of the token 16

8. Investment risk analysis 17

1. Ratings

We assign the GraphGrail AI project a "Stable" rating.

Graphgrail AI is a decentralized platform that enables the design of applications based

on artificial intelligence and blockchain technology without programming skills. On the

platform, placing tasks and receiving orders for data processing will be possible;

solutions and data may be sold on the built-in marketplace.

Work on the project has been underway since 2014. The development is being carried

out by a little known team from Russia; however, given the number of existing

developments presented to the general public in the MVP, there are no doubts about

the team's skills.

GAI tokens will be used as the internal currency of the platform. The tokenomy

mechanisms proposed by the team will help to increase the value of the token in the

long term. A buyback mechanism that is planned but not disclosed in official

documentation could provide additional token support. The amount of funds that the

team will be able to use for buyback is unclear, as there are no financial calculations.

Risks for the project lie with its technological and marketing aspects. These risks will be

detailed in the relevant chapter of this review.

2. General information about the Project and ICO

Graphgrail AI is a decentralized platform enabling the design of applications based on

artificial intelligence and blockchain technology without the need for programming skills.

Posting tasks and receiving orders for data processing will be possible on the platform;

solutions and data may be sold on the built-in marketplace.

The GraphGrail AI team has its own technological development in the field of artificial

intelligence for working with large arrays of text data. The team uses a method based

on a biological approach, offering neural networks for gathering data and model texts.

Aspects of the technology proposed by Graphgrail AI have already been created, tested

and successfully used by the project team for business and public administration tasks.

The site offers an MVP, which enables understanding the possibilities of the technology

in its first approximation.

For the Graphgrail AI project, blockchain performs not only a function for choosing the

kind of investment attracted, but also provides the project with an internal currency.

Project tokens will also store results for users received when training a neural network,

loading, and/or distributing data.

The utility token satisfies SEC conditions. Legal development for the project was carried

out by Juscutum Attorneys Association, and it has also worked out the legal risks of the

project. The legal shell company for the project has been in operation for several years,

which also contributes to reducing the risks of investing in Graphgrail AI. The jurisdiction

is the British Virgin Islands. The majority of the developers are based in Rostov-on-Don,

Russia.

Website

Whitepaper

Token: GAI (ERC-20)

Platform: Ethereum

Volume of placement: 500,000,000 GAI

Token distribution: founders — 22%, for sale — 50%, bonus fund — 25%, partners —

1%, bounty — 2%.

Round 1: Pre ICO (closed on 20/07/17)

Cap: $7000

Round 2: Pre ICO (closed on 16/10/17)

Price: $0.02

Cap: $200,000

Bonuses: 15–25%%

Minimum Buying Transaction: $10,000

Maximum Buying Transaction: $100,000

Round 3: Public sale

Volume of placement: 270,000,000

Start: 19/02/18

End: 15/04/18

Soft Cap: $2m

Hard Cap: $12m

Price: $0.1

Bonuses: 15–35%%

Raised on: $540,000

Funds allocation: 45% development, 30% marketing, 5% legal, 20% Ai Lab.

Additional:

Passing KYC and whitelist registration are necessary for participation.

Investment funds: Reliable data are absent; a well-known venture investor,

Alexander Borodich, is a co-founder.

Bounty campaign.

3. Description of the services and scope of the project

In this section, we usually discuss the existing and planned for implementation pool of

the project services and focus on the technical issues.

Variants and examples of the possible application of neural networks and machine

learning for business tasks are very broad:

Forecasting; risk assessment. (Forecasting demand, volume of sales, average

check, frequency of sales, loading of equipment for the optimization of cash

quantities, storage places and other resources).

Search for trends and correlations. Forecasting further development of a system

and predicting possible changes.

Recognition of photos, videos, audio content. Various services and online

applications with the use of recognition technology. (Example of the "LiarScan"

project for lie detection).

Machine learning for computer system dialogues. For automation of activity in

online chats, as well as for telephone operators and instant messengers.

Development of chat-bots.

The GraphGrail AI startup aims to provide easy access to the above features for various

business entities that lack relevant expertise in IT and programming.

When discussing investment in the GraphGrail AI project, it is necessary to understand

that at present a considerable portion of the technological work has already been

carried out, the blockchain mechanism for big-data storage has been developed, and

there is a working model of the analyzer using neural networks (artificial intelligence).

Moreover, the project has successful experience of monetization of its technology

working with several large companies and governmental bodies.

GraphGrail AI will provide a simple interface for creating an application model and

subsequent machine learning.

On the GraphGrail AI platform one will be able to create and train neural networks using

a user constructor. It is expected that business executives, startup owners, developers,

data experts and many others will be able to create their own applications for integration

with their own services and applications. The second possibility for the platform is a full

cycle of work with big data, from gathering and marking up to final result.

Currently, the main task for the team is to combine these services into a single

ecosystem, create a site, marketplace and mobile application.

The GraphGrail AI project will offer users four key services:

GraphGrail AI designer

GraphGrail AI labellance

GraphGrail AI marketplace

GraphGrail AI Lab.

Graphgrail AI Designer is a user builder for creating applications. The Designer can

create and train neural networks for various tasks including complex classification, using

Google TensorFlow and other tools. For business this means simple development of

chat bots, analytical products, products and services in media, determining the

authorship by style of the text, exact identification of emotions from statements.

Moreover, the designer provides an opportunity for specialists lacking in programming

knowledge to work with the platform.

Graphgrail AI Labellance — an interface for data markup. Users will be able to mark up

arrays of text data in their language and extract hidden knowledge that facilitates

management decisions. Graphgrail AI Labellance will also enable one to create

markups to order.

The Graphgrail AI Marketplace is a marketplace for language models with a possibility

for monetization and payment for requests. The marketplace will enable users to buy

and sell ready marked up datasets for training neural networks.

Graphgrail AI Lab — in this laboratory for deep machine learning, artificial intelligence

researchers and experts in the analysis of data from around the world will be able to

develop and test new and promising solutions.

In addition to the above services, Graphgrail Ai will also offer users supporting services

such as:

An automated smart contract execution system, operating through a cross-

blockchain ecosystem, webAPI and external data sources.

The implementation of ready-to-use sets of semantic categories (category-

subcategory, taxonomy, part-whole).

Implementation of blockchain for the quality control of data markup (proof-of-

quality-work).

Answering the key question of this paragraph — "Does the project need blockchain?",

we emphasise that blockchain acts as technological support for the ecosystem and of

course ensures its integrity.

The funds raised as a result of the initial offering will be spent on the completion of the

product development process. Among other things, this includes the full launch of the

platform with API access, launch and testing of the language models marketplace as

well as the support of prospective developers creating applications on the startup

platform.

4. Market review

4.1. Market analysis

Artificial intelligence is technology that is intended for the study and development of

software for intelligent machines. Artificial intelligence technologies are widely used in

various industries. Demand for solutions involving artificial intelligence is growing due to

the need for companies to increase productivity. This factor will play a key role in the

development of this market in the coming years.

The world market for artificial intelligence is segmented by solution type: Electronic

computing systems, artificial neural networks, automated robotic systems, embedded

systems and digital assistants.

The number of projects related to artificial intelligence and machine learning has grown

globally several times in the last two years. In 2015, large companies reported the

existence of 17 such projects, in 2016 another 71 projects were launched, in the first

half of 2017 — 74 projects. Thus, according to the results of 2015-2017, the total

number of initiatives has reached 162. 28 countries and 20 industries are involved in

their implementation.

85% of these projects have already been implemented, another 15% are in the planning

stages or pilot phase, and 60% of initiatives are in the public sector at this stage. In 85%

of cases, projects are carried out to order for a large business.

The United States is the leader in the number of implementations of artificial intelligence

and machine learning technologies. Second is the UK, which applies these technologies

in large investment banks, and India which uses them in work for foreign customers.

International Data Corporation (IDC) estimates that the volume of the global market for

cognitive systems and artificial intelligence technologies in 2016 amounted to

approximately 7.9 billion USD. In 2017, the market reached a volume of 12.5 billion

USD, which corresponds to an increase of 59.3% compared to 2016.

IDC analysts believe that the average annual growth rate in complex interest rates

(CAGR) by the end of 2020 will be at the level of 54.4%. As a result, in 2020 the volume

of the industry will exceed 46 billion USD.

Currently, artificial intelligence is the most important field of IT research. Electronic

intelligence, in particular, will help to analyze the huge amount of data that will be

generated by IoT devices. Experts estimate that by 2020 more than 50 billion machines

and devices capable of connecting to the network and exchanging information among

themselves will be operational worldwide.

In 2017, 1.74 billion USD and 1.72 billion USD were the share of the trade and banking

industries respectively. Researchers have spent more than a billion dollars on artificial

intelligence in discrete vs. continuous production and health care. At the same time,

trading companies not only invest more funds, but also increase their investments more

quickly. The average growth in this segment was 58.8% per year.

The most popular areas of artificial intelligence and cognitive systems are the creation

of automated customer service agents ($1.5 billion will be allocated to this) and

diagnostic and repair systems ($1.1 billion). The fastest growing segments of customer

recommendation systems (96.6% per year), public safety and emergency response

(96.2%) and intelligent process automation (69.9%).

It should be noted that about half of these investments are in software, about a third are

in services, and the equipment segment is only 18.8%.

Key trends in the artificial intelligence market:

Democratization of instruments will give access to artificial intelligence to more

companies. A recent Forrester study among organizations and professionals in

the technology field has shown that 58% of them are exploring the possibilities of

artificial intelligence, but only 12% use these systems. This is partly because they

are starting to be used only now, and because the technology is in the early

stages of development and is not easy to use. Working with these systems

requires a set of specific skills and a specific approach.

The emergence of a large number of general-purpose systems.

The economic impact of increased automation will be a topic for discussion.

Further complication of systems that prevent excess information.

Increased focus on ethics and privacy.

Thus, Graphgrail Ai Lab operates in a market with a volume of 12.5 billion USD and a

projected growth rate of 54.4% over the next 3 years. The Graphgrail AI Lab project is

aimed at solving one of the key problems for the industry — that of democratization.

4.2. Competitors

According to a study by Transparency Market Research the leading players in the

market of artificial intelligence solutions are IBM, Intelliresponse Systems, Nuance

Communications, EGain, MicroStrategy, Brighterion, Google, Microsoft, Next IT and

QlikTech International. Most projects in the field of artificial intelligence are extremely

complex and expensive for most users.

The idea of the democratization of artificial intelligence tries to interpret many projects in

its own way. Frameworks like Facebook Wit.ai and Howdy Slack are trying to become a

kind of Visual Basic artificial intelligence, promising the simple development of intelligent

conversational interfaces without the requirement of a high degree of developer training.

Tools like Bonsai, Keras and TensorFlow simplify the introduction of deep learning

models. Cloud platforms, such as the Google and Microsoft Azure interfaces, enable

one to build intelligent applications without having to worry about configuring and

maintaining an appropriate infrastructure.

Nevertheless, projects based on open decentralized platforms and blockchain

technology are now coming to the foreground. A comparison of such projects with

classic platforms is presented in the table.

Open

platform MS Azure

IBM

Watson

Yandex

Toloka

Dandelion

API

Working without

programming skills + - - - -

Ready-to-use sets of

semantic categories +

Payment

required

Payment

required

Payment

required

Payment

required

Automation of a

typical business

work-flow

+

Salaried

developer

required

Salaried

developer

required

Salaried

developer

required

Salaried

developer

required

Ease of

change/customization

of a solution for

oneself

+

It is

necessary to

order a

special

solution

It is

necessary

to order a

special

solution

It is

necessary

to order a

special

solution

It is

necessary

to order a

special

solution

Currently development of several projects similar to Graphgrail AI based on

decentralized platforms is also underway.

Opensource (Gluon) — an interface for creating machine learning models using pre-

assembled and optimized components, building blocks that can be used together with

Amazon and Microsoft platforms. Ideally, it should facilitate the process of developing

models for beginners and accelerate the creation of complex systems for experienced

professionals. Gluon is now compatible with Apache MXNet, an open-source deep

learning platform, and Microsoft is committed to its compatibility with its Cognitive

Toolkit tool.

Neuromation.io is a platform that enables creating an artificial learning environment for

neural network deep learning. These models are then used to train and improve

algorithms. The idea of Neuromation is to create a platform for the practical use of its

own scientific developments in the field of design of neural networks and artificial

intelligence systems. The main business of the platform will be associated with

compiling classified data sets for the training of neural networks. Typically, data with

manual object tagging is used to train neural networks, but obtaining such data is very

costly. Neuromation offers to replace real data sets with synthetic data, which is quite

suitable for training neural networks in certain areas of business. To generate synthetic

data, it is planned to use the computational capacities of existing cryptocurrency mining

farms based on graphic video cards.

dBrain is a decentralized blockchain platform for crowdsource data generation for

training AI-based solutions, based on neural networks. The platform carries out dataset

markup. When data is marked up, the platform finds a developer through open

competition, who creates a neural network algorithm according to the technical

specification of the customer. The developer receives a fixed payment (from 1 thousand

to 300 thousand dollars. for a private network — or a percentage of the cost of the

turnover, if the network is public. dBrain checks the finished solution and the business

connects to it through the API.

NeuroSeed is a unified ecosystem for the sale of machine learning models. Each user

can be sure of receiving paid data or payment for their intellectual property. As a result,

data exchange markets and ML-solutions are created around the platform; computing

power and various data storage methods are provided as well as a data market.

Graphgrail AI therefore operates in a highly competitive market characterized by the

presence of a large number of startups at an early stage of development, which could

compete seriously with existing market leaders.

5. Team and stakeholders

GraphGrail AI provides a single solution for analyzing text data. The Graphgrail AI team

consists of 30 specialists in data-science, natural language processing, programming,

marketing and other fields. The founder is Victor Nosko. The position of key advisor and

CMO is occupied by a venture investor, Alexander Borodich.

Key team members:

Victor Nosko — CEO and founder. Python Developer, Django framework. Data-science

specialist, NLP stack: NLTK + Celery + Pymorphy2 + GLRparser, etc. Victor has over 6

years of experience in development and deep learning, and is experienced in Google

TensorFlow.

Alexander Borodich — Venture investor, CMO. Futurist, business angel, founder of

VentureClub, MyWishBoard, MyDreamBoard, and SuperFolder. Partner at Future

Action, founder of VentureClub.ru, and Universa. Universa held its ICO in 2017; it was

subjected to an active information attack online, which damaged the reputation of Mr.

Borodich in the crypto community.

Anton Smetanin — Fullstack web developer. He is responsible for backend

development. He has more than 7 years’ experience in this field. Main languages and

frameworks used: PHP (Yii), Python (Django), Javascript.

Alexander Gusarin is a Python and Data Science developer. Responsible for the

development of machine learning programs and Python programming.

Zakhar Ponimash — Consultant on neural networks. He works with neural networks and

artificial intelligence. Game developer, based on the XNA framework, TCP/IP chat, bot

chat, text comprehension systems.

Semyon Lipkin — Developer of Python and Data Science. Sphere of activity —

development of algorithms of machine learning using the Python language.

Maria Tarasova — Journalist. Candidate of philosophical sciences, specializing in

simulation, data mining and statistical data analysis. She was awarded a scholarship by

the President of the Russian Federation and the Government of the Russian Federation

for major contributions to science; she is the author of more than 60 research works on

the modeling of social processes, an active participant in 5 grants from the Russian fund

Fundamental Research, participant in more than 10 national and international

conferences.

Marina Parinova — HR manager. Responsible for IT recruitment.

Nikita Buyevich — Frontend Developer. He is responsible for creating user interfaces.

Once again we emphasise that the established team has successfully completed a

number of projects for business and government. Most of the team has solid experience

in the implementation of scientific and business projects. The team has all the

necessary relevant experience to implement its project. However, we identify marketing

and finance as potential shortcomings.

6. Token analysis

Graphgrail AI is selling GAI tokens during the ICO. GAI is a utility token that acts as the

internal currency for the system.

To be able to access the system, users (primarily business users) will have to purchase

a certain amount of GAI tokens — from 5 to 10,000. These tokens can then be spent by

the user on internal services of the platform — collection, cleaning, data marking,

custom settings for training a neural network, etc.

Users who receive tokens as payment for their services will be able to convert them to

fiat or other cryptocurrency. However, due to an obligation for users to make a one-time

purchase of a relatively large volume of tokens, the project will likely achieve a

permanent excess of demand for the token over its supply, provided that the product is

successfully implemented.

The token has no other functionality.

By and large, the team could replace the GAI token with any liquid cryptocurrency and

use it as the internal currency. In other words, GAI tokens should primarily be

considered as a mechanism for funding the project.

7. Analysis of factors affecting the future value of the token

We have already noted that the proposed mechanism obliging users of the platform to

buy a certain amount of tokens at a time from the market to gain access to the

platform's functionality, will help to permanently ensure demand for GAI exceeds

supply. This is only given the emergence of a steadily growing utility demand.

In this regard, optimism is engendered by the fact that work on the project has been

underway since 2014 according to the roadmap; the team has already achieved certain

developments, and new services will be introduced with enviable regularity. Key

elements of the platform will start functioning before October 2018, which should ensure

the relatively short-term appearance of infrastructural demand for the token.

The project team has shared with us its plans to buy back tokens from the market not

more often than once a quarter. Bought back tokens can be either burnt or used for

platform purposes.

The burning of tokens could have a positive impact on their price, whereas a return to

circulation is unlikely. Moreover, GraphGrail AI has a reserve fund consisting of 25% of

the initial issue. The plans of its use as yet exist only as a first approximation — it is

suggested that it will be used for attracting users and developers to the platform as

additional motivation and for accumulation of datasets, libraries, algorithms, i.e.

intangible assets for the benefit of the team. The terms of use for this fund are not

disclosed; however, we believe that a quarter of the total issue is too large a figure and,

in the absence of restrictions on the team, the reserve fund should be considered as a

risk to the future dynamics of the token's price rate.

8. Investment risk analysis

We have described the risks for the token in the previous chapter. Below we will

concentrate on the risks of the project itself and its ICO.

In such projects there are always risks of a technological nature, i.e. risks of technical

realization. In this case, the team has long been working on the project which increases

the likelihood of success. However, it remains unclear whether the innovations being

developed will be applicable in practice in the near future.

Another project risk is the fact that the team has not involved anyone prominent in the

crypto community or business environment. An exception is Mr. Alexander Borodich,

whose previous project, Universa, faced active criticism, albeit ambiguous in nature.

However, GraphGrail AI’s hard cap is also small compared to other ICOs, so this risk

also should not be considered significant.

Marketing and promotion of the ICO are also among the risks of the project. At this

stage the traditional metrics for estimating activity of the ICO campaign (Telegram,

Bitcointalk, etc.) are at an extremely low level. Publications in the press about the

project are also few and far between. The project is niche and specialized. In this sense,

there is still a high probability that many potentially interested parties will not know about

the ICO.

The Graphgrail AI project did not provide a financial model, which prevents us

estimating the projected costs of maintaining the operating activity, or assessing the

degree of dependence of the project’s viability on the amount of funds raised during the

ICO.

The information contained in the document is for informational purposes only. The views

expressed in this document are solely personal stance of the ICOrating Team, based on

data from open access and information that developers provided to the team through

Skype, email or other means of communication.

Our goal is to increase the transparency and reliability of the young ICO market and to

minimize the risk of fraud.

We appreciate feedback with constructive comments, suggestions and ideas on how to

make the analysis more comprehensive and informative.