Machine Learning & 
Artificial Intelligence 
Software Development

We help companies maximize their agility by creating custom AI-powered solutions that automate business processes and increase enterprise effectiveness like you’ve never imagined

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Machine Learning Artificial Intelligence Software Development
85%

of all customers’ relations with businesses will be managed without humans involved by 2020.

97%

mobile users are already using AI-powered voice assistants.

$1 billion

of the Netflix budget was saved with the use of Machine Learning.

72%

execs believe that AI will be the most significant business advantage of the future.

Our ML-powered projects

FoodbookNewsCropPrintlix
Foodbook
Foodbook

A neural network-based application that combines two main features — meal recognition by photo and a very accurate calorie counter.

NewsCrop
NewsCrop

Optical Character Recognition program that finds images in newspapers to automatically cut them out and archive. An all-encompassing article digitalization tool that can serve as a basis for a mobile app of a printed newspaper.

Printlix
Printlix

Application with unsupervised learning and data clustering technologies on board that analyzes user’s images and suggests the most suitable one for quality printing.

What’s Machine Learning?

ML is subdivision of AI and a set of algorithms that enable software to act and learn like humans do. Today, Machine Learning is an absolute necessity to include in a company’s innovation strategy, since precise data analysis has already became fundamental for business growth.

Higher labor productivity

Higher labor productivity

Better customer experience

Better customer experience

Valuable data insights

Valuable data insights

High-tech market advantage

High-tech market advantage

Insights-driven business

Your company has been collecting data for years. We’ll give you a tool to turn it into sales-generating information.

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Insights-driven business
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Decision-making support

AI-powered software brings accurate data analysis that can increase team’s efficiency by 40% and eliminate human errors.

Real-life ML software examples

72% of business leaders

defined AI as a “business advantage” and admitted it to become fundamental in future.

AI will generate $1.2 trillion more

for insights-driven businesses comparing to the incomes of less informed peers by 2020.

Amazon Alexa

A consumer-facing virtual assistant that can be used to perform such tasks as playing music, setting alarms, making to-do lists, and other things a user would typically do manually. It works due through natural language processing technology and actually talks to a user with female voice in a conversational manner. It also used as a part of IoT-powered smart homes.

Erica by BoA

A financial digital assistant launched by Bank of America. It was created to make banking easier for people while eliminating manual work. Erica is available 24/7, which means there’s less need in night-shift human workers. Erica helps to perform transactions, sends reminders, monitors charges, etc. In 2019, it surpassed 6 million users and completed 35 million online requests.

Watson

A cognitive computer system that was initially developed to answer questions posed in natural language. It was later transformed into a powerful cloud platform offering enterprises ready-to-use AI services. It allows implementing ML applications in basically any business and automating the working process with them.

Einstein

A collective name for all AI-related technologies and services created by Salesforce. It handles data gathering and preparation to form an infrastructure needed to embed ML models in client’s Salesforce application. Einstein platform encompasses computer vision and NLP apps that are accessible to any Salesforce clients. With Einstein, companies can get data insights and customize datasets improving the outcomes overtime

How We Develop
Software Solutions

We solve business challenges with innovation applying best-in-class expertise and personal approach to every single project under development.

Discovery stage
Discovery stage

We start with in-depth analysis of client’s business needs, clarifications of future product requirements, and estimation of time frames and project costs

UX/UI stage
UX/UI stage

Having thoroughly analyzed the desired product functionality, we create visual design for the application and determine the most frequent UX scenarios to polish it.

Development stage
Development stage

Following Agile approach to development, we provide new piece of functionality every two-four weeks to get client’s feedback and improve anything immediately if needed.

Who's using Machine Learning

  • Sales & Marketing
  • Finance
  • Healthcare
  • Agriculture
  • Social Media
  • Logistics
  • Oil and gas
Sales & Marketing

The way ML deals with unstructured client data opens endless opportunities for narrowly targeted marketing campaigns, personal offers, and digital shopping assistance for better user experience.

Finance

Machine learning tech stack is used to detect fraudulent activity in banking, as well as to assess credit risks and assist customers with transactions.

Healthcare

Accurate record and fast access to patients’ information is the tip of the iceberg when it comes to ML applications in healthcare. Diagnosis prediction and visualization, behavioral modification, and clinical research are also on the list.

Agriculture

Robots for harvesting, field inspections with drones, and see and spray system to fight pests are amongst the most productive applications of AI subsets in agriculture.

Social Media

Social networks powered by AI has been around for years. Currently, AI for social media is focused on web content testing and optimization and user behavioral patterns recognition.

Logistics

Logistics, supply chain, and transportation are known for being amongst the most challenging and hard-to-manage industries. With ML, accurate delivery scheduling and route planning do not require countless man-hours anymore.

Oil and gas

Oil and gas industries use AI subsets for distant source explorations at the bottom of the ocean and digital assistance for both clients and field workers. Also, some companies planning to add AI robots for solving observational tasks like data gathering and manufactory checks.

What are some popular machine learning methods?

The supervised learning method is based on input-output examples and works as follows: an engineer loads a system with dataset of labeled samples so that it would catch correlations and use them as an experience to rely on when dealing with unseen before data inputs.

Unsupervised learning is aimed at finding unknown patterns in data that has no labels or structure, and to define probability densities in it. It’s applied in cases like banking transactions, when the system cannot be told right answer in the first place and each case to analyze is unique.

The other ML methods include semi-supervised and reinforced learning, regression, classification, clustering, dimensionality reduction, ensemble methods, and word embeddings among others.

Programming Languages for Machine Learning

  • ‐ Python
  • ‐ C++
  • ‐ C#
  • ‐ Java
  • ‐ R
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Terms Explained: Artificial Intelligence, Deep Learning, Machine Learning, Data Mining, Traditional Programming
Artificial Intelligence
Artificial Intelligence

An area of computer science that is based on cognitive computing and signal processing. It approaches to build a human-like machine that could mimic human behavior, such as thinking, learning, and decision-making actions.

Machine Learning
Machine Learning

A subset of AI that lies in a study of models and algorithms a machine needs to become capable of dealing with tasks it wasn’t programmed for. It can also be defined as a set of algorithms for a machine to put data through to reveal certain assumptions. This term includes Deep Learning.

Deep Learning
Deep Learning

Deep learning is a subfield of machine learning that lies in data processing through artificial neural networks. Computer neurons receive the input information and transform it into a command/conclusion that seem to be imperceptible for humans.

Data Mining
Data Mining

A practice of processing already existing datasets in order to generate new information and identify unknown before assumptions. Often involves statistical methods and data manipulation practices.

Traditional Programming
Traditional Programming

A manual implementation of logic in a form of software solution to facilitate predefined computer operations. Unlike ML, the result of a running software fully depends on the algorithms written by programmers.

Let’s create together

AI subsets bring ground-breaking benefits to the business landscape. Click here to find out, how your company can be transformed with it.

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