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74% of companies hope to improve business outcomes with AI, but only 1 in 3 projects are successful. 

GNY ‘s Mission is to securely unlock the hidden value in data through advanced machine learning. For some companies this also involves building a system that integrates blockchain, for others it does not. Regardless, the first steps are the same. It all starts with your data.


Deploying machine learning intelligently demands a complex understanding of your current data position, your goals, expectations of price, time to deployment, and results.

That’s why we designed the GNY Data Diagnostic.

GNY Data Diagnostic

The data exploration service is offered as a flat fee service in the AWS Marketplace, and a free consultation allows clients to explore the service with no obligation. AWS Marketplace is a digital catalog with thousands of software listings from independent software vendors that make it easy to find, test, buy, and deploy software that runs on Amazon Web Services (AWS).

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AWS Marketplace and the AWS Marketplace logo are trademarks of Amazon.com, Inc. or its affiliates

The Goal of the GNY Data Diagnostic is marry your business goals with data driven Machine Learning outcomes, and then design a data system that will allow you to achieve both. As a firm that offers both tailor-made ML solutions, as well as open source blockchain powered solutions we provide value to many types of organisations.

Our Data Diagnostic is comprised of 4 main sections parts:

  1. Education

  2. Discovery

  3. Data Analysis

  4. Recommendations

The GNY Data Diagnostic analyzes a company’s datasets and detects if the historical data is strong enough for effective ML, or if weak or inconsistent datasets are skewing the predictions and weakening the business. GNY’s Data Diagnostic team offers everything businesses need to become AI-ready, from education about AI basics and how predictive analytics works, to learning about the company’s data collection practices to offer a tailored solution.

 

The result includes a detailed analysis of the historical data, what needs to be done to prepare to support ML, as well as an analysis of the digital architecture's ability to support the company’s business goals.