Technology Category
- Application Infrastructure & Middleware - Blockchain
- Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries
- Cement
- Equipment & Machinery
Applicable Functions
- Procurement
Use Cases
- Clinical Image Analysis
- Track & Trace of Assets
The Customer
Vasiliy Karpitski and Alexei Dulub
About The Customer
The customers of CheckNFT.iO are primarily NFT investors, creators, and businesses in the NFT field. These customers are looking for a reliable and intelligent solution that can help them make informed investment decisions in the NFT market. They need a platform that can provide them with actionable data on NFT collectibles, their provenance, and ownership. They also need a platform that can help them avoid scams and other fraudulent activities in the NFT market. In addition, the platform also caters to the needs of analytics firms that require quick access to a large array of structured data and the results generated by machine learning models for analytical purposes.
The Challenge
Vasiliy Karpitski and Alexei Dulub, entrepreneurs and blockchain enthusiasts, identified a significant challenge in the rapidly growing NFT market. The NFT market, which hit $17.6 billion in sales in 2021, has attracted a large number of creators, businesses, and investors. However, the rapid growth of the market has also led to an increase in scams and fraudulent activities such as blacklists, wash trades, and duplicates. This has made it difficult for NFT buyers, creators, and businesses to make quality investment decisions and avoid risks. To address this challenge, the entrepreneurs decided to develop a smart solution that would help NFT investors make informed decisions by providing them with actionable data on NFT collectibles, their provenance, and ownership.
The Solution
The solution to this challenge was the development of CheckNFT.iO, a platform that collects and analyzes large amounts of data from the blockchain network and machine learning models. The platform allows users to browse, compare, and analyze data behind NFT collectibles. It is equipped with an AI engine that analyzes NFT’s history on the blockchain and monitors newly created blocks in real time to provide users with potential copies or IP exploits. The platform also includes advanced analytics tools like marketplace analytics, top NFT gems, and more. To prevent scams and other financial manipulations, the platform uses an AI algorithm that detects fraudulent smart contract creations, mints, and other suspicious activities. The platform also ensures intuitive navigation so that both NFT newbies and professionals in the field can easily use the service.
Operational Impact
Quantitative Benefit
Case Study missing?
Start adding your own!
Register with your work email and create a new case study profile for your business.
Related Case Studies.
Case Study
Smart Water Filtration Systems
Before working with Ayla Networks, Ozner was already using cloud connectivity to identify and solve water-filtration system malfunctions as well as to monitor filter cartridges for replacements.But, in June 2015, Ozner executives talked with Ayla about how the company might further improve its water systems with IoT technology. They liked what they heard from Ayla, but the executives needed to be sure that Ayla’s Agile IoT Platform provided the security and reliability Ozner required.
Case Study
IoT enabled Fleet Management with MindSphere
In view of growing competition, Gämmerler had a strong need to remain competitive via process optimization, reliability and gentle handling of printed products, even at highest press speeds. In addition, a digitalization initiative also included developing a key differentiation via data-driven services offers.
Case Study
Predictive Maintenance for Industrial Chillers
For global leaders in the industrial chiller manufacturing, reliability of the entire production process is of the utmost importance. Chillers are refrigeration systems that produce ice water to provide cooling for a process or industrial application. One of those leaders sought a way to respond to asset performance issues, even before they occur. The intelligence to guarantee maximum reliability of cooling devices is embedded (pre-alarming). A pre-alarming phase means that the cooling device still works, but symptoms may appear, telling manufacturers that a failure is likely to occur in the near future. Chillers who are not internet connected at that moment, provide little insight in this pre-alarming phase.
Case Study
Premium Appliance Producer Innovates with Internet of Everything
Sub-Zero faced the largest product launch in the company’s history:It wanted to launch 60 new products as scheduled while simultaneously opening a new “greenfield” production facility, yet still adhering to stringent quality requirements and manage issues from new supply-chain partners. A the same time, it wanted to increase staff productivity time and collaboration while reducing travel and costs.
Case Study
System 800xA at Indian Cement Plants
Chettinad Cement recognized that further efficiencies could be achieved in its cement manufacturing process. It looked to investing in comprehensive operational and control technologies to manage and derive productivity and energy efficiency gains from the assets on Line 2, their second plant in India.
Case Study
Integration of PLC with IoT for Bosch Rexroth
The application arises from the need to monitor and anticipate the problems of one or more machines managed by a PLC. These problems, often resulting from the accumulation over time of small discrepancies, require, when they occur, ex post technical operations maintenance.