Machine
Learning | AI

Our Services

Global Mediator helps leverage the data in your business to drive change or new business insight. Machine Learning and Artificial Intelligence (AI) leverage infrastructure resources with volumes of data in order to enable different business systems and processes to be autonomous and accurate in their prediction, determination and decision making. Depending on your data (type, quality, volume and accessibility), your business requirements (service improvement, product design input, insight) or specific need for innovation; Global Mediator can help you get started.

Machine Learning | AI Overview

The finance function owns that data on a transactional level, monitors the data for risk purposes, and turns those data sets into insights for operational and strategic decision-making. Increasingly, the ability to harness deeper, more real-time insights from that data is a becoming a competitive necessity; conclusions made in every function of the organization run the risk of being flawed without timely, detailed, and accurate financial information. Machine Learning and Artificial Intelligence leverage large volumes of data in order to enable systems to learn how to improve by themselves. By feeding data, adjusting output requirements all without being explicitly programmed, we can train systems to give us the results we expect (review, instruct, iterate) and let it go about its’ business. Machine Learning and AI use algorithms to parse data without relying on rule-based programing which in turn enable businesses to make better and faster determinations and predictions. For CFOs that are being swamped in data coming from ERP, payment processing, business intelligence, and a host of other systems built on structured databases, this type of decision-making support is becoming increasingly important.

What
is it?
  • Automated and guided learning of machines on business data for new and innovative views on the data that is the foundation of a business
  • Usage of large quantities of data to:
    • Model data
    • Run data models
    • Serving Content
    • Provide predictions for specific business process/task
  • Automation of learning by machines, in order for the business to offer “smart” solutions to its customers, or new “smart” insights into the business for its managers and executives
Who
is this for?
  • Companies that get extraordinary amount of data from marketing databases, transactional records and public information records
  • Businesses seeking to embed machine learning into products to allow for quicker and more effective decision-making
  • CFOs that need machines that over time can learn to distinguish what data points are important from those that aren’t
  • CMOs that want to extract insight from the machines to allow customers to optimize their processes and predict what they will enjoy
  • Read More
Prerequisites
How it works
steps

Identify business problems

Establish goals and success metrics

Gather and assemble data

Define technical constraints and expected business outcome

Practices
  • Start with a business problem statement and establish the right success metrics
  • Data
  • a. Assessment
  • b. Verification
  • c. Verbosity
  • d. Veracity
  • Don’t move your data – move the algorithms
  • Assemble the right data
  • a. Business domain — these are the people who know the business
  • b. Information technology — the people who have access to data
  • c. You need the active participation
  • d. Create new derived variables
  • Consider the issues and test before launch
  • Deploy and automate enterprise-wide
People
These are some of our people specialized and working with this.
Oleksandr Klymenko
MS NAV Senior Software Development Engineer
Oleksandr Paziuk
Python Developer
Yuriy Yastrub
Python Developer
Tetiana Falaleeva
Robotics Process Automation Software Developer
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