OpenProductDb project whitepaper

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In conditions when online trading has became truly massive, accurate structured descriptions of goods are no less important than advertising. In reality, any investment in advertising can be devalued by only several errors in the description of the properties of the goods.

Even just correct descriptions of goods are no longer sufficient for a full work of online store, because nowadays, the maximum efficiency is achieved through the use of descriptions in the form of structured data, such as JSON-LD with schemes.

How actual is this problem, and if everything is really so seriously, is there any solution at the global level?


The lack of detailed descriptions of the product is one of the most obvious problems in the modern online trading.

Insufficient description of the product leads to the fact that the buyer chooses to buy that product on the trading platform, which is more in accordance with the requirements of the buyer.

If the product description contains errors, the store risks its reputation, caused by the dissatisfaction of the buyer, especially after the goods are already sold.

A small online store often just doesn't have enough employees, who are able to put the catalog of goods in order.

As a solution, various sources of descriptions are used, which on the one hand does not guarantee the correctness of the information, and on the other hand, in most cases, do not solve the issue of the legality of using these data.

At the same time, if at the initial stage of development of online trade was sufficient simple text description, nowadays this is not enough. Search systems are increasingly moving away from the concept of "text search" and more passes to the intellectual search, which is based on the knowledge bases, formed from structured data (firstly, supporting standards of data schemes with

On the other hand, SEO experts are convinced that the use of such structured data acquires the character of necessity, and it can provide the long-term competitive advantage that is the goal of search engine optimization.

Most of the major Internet platforms in one way or another are already using such structured data, but for many reasons (and, first of all, lack of complete data) is very limited.

Is the problem relevant for the largest platforms?

We conducted a study for which the descriptions of goods for the most popular categories on the largest trading platforms were analyzed.

  1. Amazon

    • Mainly the text descriptions are used

    • Some limited number of properties are specified in the interior structured view, which provides the ability to compare goods.

    • According to a difference between the property values for one product model, each seller uses his own description, not based on the standart one.

    • One of their examples - the same phone model from different vendors has different property meanings. Moreover, in one of the cases, the difference in the specified meaning of the "mass" is 6 (!) times.

  2. Ali-express

    • Mainly the text descriptions are used

    • For additional description a combination of unstructured text and images with descriptions of properties is used.

    • A number of properties are specified in the interior structured representation.

    • According to a difference between the property values for one product model, each seller uses his own description, not based on the standart one.

    • With a large number of sellers, the difference in descriptions is so obvious, that raises doubts that it is a question of the same goods.

  3. Yandex Market

    • Employees of the company initially took the issue seriously, they developed a special markup language for the products "YML (Yandex Market Language)"

    • Despite this, most of the goods do not contain structured property meanings.

    • According to a difference between the property values for one product model, each seller uses his own description, not based on the standart one.

    • In addition to the problem with the descriptions, there is clearly a problem with choosing a category goods. For example, in the section "Automobile transport > Cars", the following products are presented:

      • Additive to oil
      • Toy car model
      • Mobile stall
      • Harness for carrying dogs

As the examples show, even for large trading platforms this problem is more than relevant.

And if this problem is actual even for large trading platforms of the world level, for young and less global sites this problem takes a massive and almost insolvable character.

Existing solutions

As the problem is not new, it is natural to assume that some steps have already been taken to solve it. Moreover, often, the attempts to solve the problem speaks about the seriousness of the problem and the necessity of the solution.

We found the following variants to solve the problem:


    • Non-profit project

    • Contains a minimal description (brand, product name) and GTIN-13 code.

    • For downloading is offered a version from 2014-01-01, judging why the project nowadays is practically not working.


    • Non-profit project

    • 4521 product in the form of CSV (as of 2018-02-02)

    • Minimum description and GTIN-14 code. Several common properties ("size").

    • The database is updated sometimes, but the speed of refill is very low.


    • Non-profit project

    • One of the few working projects.

    • Data only for food products.


    • Does not contain data (as of 2018-02-02)

As the examples show, with rare exceptions, noncommercial orientation of the project rarely leads to a fully functioning service.

We consider that the main reason for this is lack of interest both for the authors of the project and for the authors of the data.

Our solution

Our solution is to create a global database of goods, using the latest technologies both in terms of product ontology ( standards), and in relation to their taxonomy (division into categories - GPC standard).

At the same time, despite the openness of the base itself, we consider it important and necessary to commercialize the project, both to ensure the very existence of the project, and to ensure maximum data quality through stimulating authors.

Thanks to the use of crypto-tokens (which are the basis of the organization of all aspects of the service), and orientation to their maximum use and systematic increase in their value, we provide a regime of maximal profitability for investors.

In this case, even in terms of the openness of the service data, we are oriented not only for the benefit of the community, but also thanks to the chosen data license (CC-BY), we are counting on the growth of popularity and reputation of the service itself.

Commercial offerings

Sellers of goods can place their offers on the product description page.

At the same time, the minimum price of such an arrangement is initially defined as the equivalent of 1 USD per item per month.

On the product page the offers are shown according to the location of the user.

Considering the globality of the service, we understand that we can not guarantee a universal solution to the problem of different price categories for different products, therefore we consider the best solution is to use natural competition processes instead of manual price regulation.

I.e. despite the same minimum placing price, for all categories of goods and geographical location of the seller, in reality, competition of sellers for the first places in the placement, will result in a conformity of placement price to conditions of the local market, depending on demand and offer for a particular product.

This approach will maximize the chances both of a seller in a multimillion megapolis, and the seller in the town with a thousand inhabitants, that works on the mass and globality of our system.

Public data

All data in our system is available for use under the CC-BY license.

This means that both an individual entrepreneur and a representative of a commercial structure can use the data of our system for any purposes, including commercial purposes, if some simple conditions are followed - an indicator of the authorship of the original version of these data.

Data is provided in the form of JSON-LD objects, which can be used immediately (without changes) for SEO purposes.

In addition, we plan to create ready-made plug-ins for the most common CMS used for e-commerce.


In many ways, thanks to the use of crypto-token, our project has became possible.

It is used:

  • to finance the project itself on the basis of ICO

  • for payments to content authors, for stimulating high quality of data

  • for voting on virtually all aspects of project development, starting with the evaluation of data quality and ending with the time and size payments to investors on income

  • to stimulate the use of the token, discounts will apply when paying for placements with tokens

  • to ensure the growth of the value of the token, part of the income received not in the form of tokens, will be spent on buyout of tokens in the market

Token parameters

  • Value

    • 1 token = 0.001 ETH

    • 1 ETH = 1000 tokens

  • Amount:

    • 50,000,000 tokens
  • Soft cap (minimum amount of success):

    • 1,000,000 tokens
  • Hard cap (maximum number for sale):

    • 27,500,000 tokens

Token distribution

Target Percents Tokens
Sale: Pre-sale 5 2,500,000
Sale: ICO 50 25,000,000
Founders 25 12,500,000
Reserve for authors 10 5,000,000
Consultants 5 2,500,000
Bounty program 5 2,500,000

Token Sale

The sale of tokens is made in 2 stages.


At the first stage (pre-ICO or pre-sale), 5% of tokens are sold at a discount in 50%. This stage is necessary to cover the costs related to the main period of sale tokens (ICO), as well as to support work on the project before the early version of the product. The funds received at this stage can be immediately transferred to the required form and used for the purpose indicated above.


The second stage is a full-fledged ICO, with the reservation of funds before the end. The sale of tokens at this stage starts at a discount of 25%, then the discount percentage decreases, down to 0%. In total at this stage, up to 50% of all released tokens are realised.

The second stage is considered successful in achieving a minimum volume of sales (soft cap), and continues up to the full realization of the entire volume of tokens allotted for this.

The funds received in the second stage can only be used after the release early version of the product.


The head of the team

  • Nedorezov Gennadiy

    • Role: Founder

    • Country: Russia

    • Candidate of Economic Sciences

    • Business Developer

  • Aleksej Jerenkevic

    • Role: Founder

    • Country: Germany

    • Candidate of Economic Sciences

    • Business Developer

  • Antipov Sergey

    • Role: Technical Director

    • Country: Russia

    • Has Over 25 years experience in IT field

    • Microsoft MVP 2008

Technical Team

  • Dmitry Divitay

    • Role: Head of the backend department

    • Country: Russia

  • Ilya Ivanov

    • Role: Head of the frontend department

    • Country: Russia

  • Pavel Pochivalin

    • Role: Head of the UI/UX department

    • Country: Russia

The full composition of the technical team will be published after the release of the final version of the product.


  • Natalia Petukhova

    • Role: eCommerce expert

    • Country: Russia

    • More than 10 years of expertise and entrepreneurship in the field of e-commerce

  • Oleg Luchkov

    • Role: Business expert

    • Country: Russia

    • Candidate of Economic Sciences

    • Member of the Union of Machine Builders of Russia

  • Dmitri Nesterov

    • Role: Business expert

    • Country: Georgia

    • Deputy Chairman of the Association of Tourism Development of Adjara

We are open to suggestions and will be happy to get into the team as official, and informal consultants from all around the world. To attract consultants, we have reserved 5% of all issued tokens.

Road map

  • 2017

    • February - June
      • The main idea of the project
      • Market research
      • Search for like-minded people
    • From July to September
      • Understanding the need to use crypto currency and tokens
      • Decision on entering ICO
      • Formation of the team core
      • Formation of technical requirements
    • From September to November
      • Formation of a technical team
      • Work on the first prototype
    • From November to December
      • Working prototype
      • Work on the alpha version
  • 2018

    • January
      • Working alpha version
      • Preparation for pre-ICO and ICO
    • February
      • Start pre-ICO
        • Limited pre sale of 5% of tokens
        • 50% bonus
    • April
      • Start ICO
        • Sale of 50% of tokens
        • Bonus 25% with a weekly decrease
    • July
      • Release of the early version
        • Full access for investors
        • Limited public access
    • September
      • Release of the public version
        • Full public access
        • Output of a token to exchanges
  • 2019

    • From March to April
      • Beginning of payments to investors
        • Conducting investor voting on payments
        • First payments to investors