Outrank the competition with machine learning solutions


Machine learning solutions are transforming business capabilities. Reimagine what's possible, and leave the competition behind.
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Gain a competitive advantage using machine learning applications

The use of machine learning in business is increasing rapidly. Smart companies are harnessing the value of their data in machine learning models to reduce costs, optimize processes, and increase customer satisfaction.

AI and machine learning have the potential to reshape industries – ML algorithms can be used in applications across practically all sectors, from eCommerce to finance, healthcare to education, and cybersecurity to charity.

Netflix

Netflix saves $1 billion each year thanks to machine learning algorithms

Engaging subscribers with tailored content

Netflix’s recommendation engine uses dozens of algorithms to compare viewers’ preferences with similar customers around the world. This capability allows the company to engage its subscribers with tailored content and improve loyalty to its service.

We think the combined effect of personalization and recommendations save us more than $1B per year.
Carlos A. Gomez-Uribe

Carlos A. Gomez-Uribe

Former VP of Product Innovation at Netflix Inc.

Machine learning can be used across practically any sector or industry


Machine learning can support your business in many ways:
  • Increase sales

    49% of customers are willing to purchase more frequently when machine learning is present.

  • Improve productivity

    Machine learning technologies are projected to increase labor productivity by up to 40% by 2035.

  • Analyze large volumes of data

    ML gives apps the ability to learn and improve over time. It is extremely adept at processing large volumes of data quickly and identifying patterns and trends.

  • Improve customer satisfaction

    75% of enterprises using AI and machine learning tools enhance customer satisfaction by more than 10%.

Let’s work together

Machine learning projects don't have to be complex – we’ll help you choose the right solution


There are a variety of machine learning solutions that can be tailored to different business needs. To make sure you get the most out of your project, our data science experts will select the best approach for the specific needs of your business and your market.
  • Data Engineering

    Prepare your data to make the most of AI algorithms.

  • Data Science

    Uncover meaningful insights to improve your product or service.

  • Recommender Systems

    Create a personalized experience for every user thanks to an accurate recommendation system.

  • Natural Language Processing

    Build natural interactions with your users and recognize patterns in unstructured data.

  • Computer Vision

    Automate difficult decision-making processes based on images.

  • Audio Recognition

    Identify patterns in audio data, enabling voice communication using a range of devices.

Let’s work together

A robust process is our key advantage

Over the years, our team has tested and implemented a transparent and efficient workflow for machine learning projects. Our processes ensure our customers receive more reproducible results faster and in a more flexible way.

Our workflow focuses on three stages:

How we work?

  1. We ensure that we have everything the model will need during training.

  2. We create a model capable of producing predictions.

  3. The model is connected to your application.


What is machine learning?

Machine learning is a set of artificial intelligence techniques that gives web and mobile applications the ability to learn, adapt, and improve over time. It does this by processing vast amounts of data, identifying trends and patterns within it – most of which would not be apparent to a human being – and then making decisions and taking actions to help meet specific objectives.

Why do you need machine learning?

Machine learning solutions open your business up to a wide variety of new opportunities. You can use machine learning models to personalize your customer experience, automate processes, gain deeper insights with advanced analytics, and deploy digital solutions that will change the way customers interact with your product.

Machine learning is widely applied to business problems, reducing costs and increasing customer satisfaction. ML algorithms can be used in applications across practically any industry or sector – from eCommerce to finance, healthcare to education, and cybersecurity to charity services.

What are the best examples of machine learning?

Machine learning solutions are being used in various business sectors – both B2B and B2C companies can benefit from it.

Amazon uses an ML-powered recommendation engine that drives 35% of its total sales. Thanks to the AI-Bot Harry, AXA saves roughly 17,000 man-hours a year. At the same time, Vodafone noticed a 68% improvement in customer satisfaction after introducing its machine learning chatbot TOBi.

American Express and PayPal use machine learning models to quickly analyze millions of transactions and data points, giving them real-time fraud detection capabilities. These advanced digital tools allow customers to resolve problems with suspicious transactions almost instantly.

Researchers based at UCLA managed to identify cancer cells with greater than 95% accuracy after equipping a special microscope with machine learning algorithms.

Where can ML solutions be used?

Machine learning models are used in a range of industries. Businesses are using models to improve performance through process automation, predictive analytics, and anomaly detection, among a variety of other use cases.

For example, eCommerce and marketing leverage ML algorithms for their recommendation engines to provide better customer experiences. Hedge funds use ML tools to forecast stock prices, while insurance companies use advanced techniques to calculate risk more accurately. Banks and other financial institutions are able to detect suspicious transactions using fraud detection models. Medical companies use digital tools and deep learning approaches to diagnose medical conditions based on sets of symptoms.

Are machine learning services right for your business?

Machine learning projects are often high-risk projects due to their complex dependencies on data. That is why top companies offering machine learning services conduct feasibility studies to reduce the risk before engaging in a project. In this way, they ensure that sufficient data is available and that predicted outcomes are in line with the project goals.

World-class specialists at your disposal

Our developers are open source contributors with more than 220 repositories on GitHub.
We constantly invest in developing new digital technologies and testing solutions in our R&D department, sharing our experience and expertise both on expert blogs and at various tech industry conferences such as IGARSS, AAIA, and MICCAI.

Our team has provided machine learning services in a variety of engagements, including many end-to-end projects.

Our partners on working with Netguru

  • My experience working with Netguru has been excellent. Outstanding software teams are resilient, and our developers at Netguru have certainly proven to be that. Our Netguru friends have become as close to team members as possible, and I am grateful for the care and excellence they have provided.

    Gerardo Bonilla

    Product Manager at Moonfare
  • Netguru has been the best agency we've worked with so far. Your team understands Kelle and is able to design new skills, features, and interactions within our model, with a great focus on speed to market.

    Adi Pavlovic

    Director of Innovation at KW
  • Working with Netguru has been a fantastic experience. We received a lot of support in terms of thinking about how we track metrics, how we design this properly, and how we build the architecture. We are extremely grateful for making our platform what it is today.

    Manon Roux

    Founder at Countr

Netguru in numbers

  • 10+

    Years on the Market

  • 600+

    People on Board

  • 600+

    Projects Delivered

  • 67

    Our Current NPS Score

Delivered by Netguru


We are actively boosting our international footprint across various industries such as banking, healthcare, real estate, e-commerce, travel, and more. We deliver products to such brands as solarisBank, PAYBACK, DAMAC, Volkswagen, Babbel, Santander, Keller Williams, and Hive.
  • Self-care mobile app that lets users practice gratitude

    Shine

    $5M Granted in funding

  • Investment platform that enables investment in private equity funds

    Moonfare

    $28M Granted in funding

  • Data-driven SME lending platform provider

    Finiata

    $20M Granted in funding

  • Lead generation tool that helps travelers to make bookings

    Tourlane

    $47M Granted in funding

Machine learning services: All your questions answered


Not sure how machine learning services can deliver value to your business? Check out some of the most common questions asked by our clients.
How do you estimate a machine learning project?

It can be difficult to provide a ballpark figure for machine learning solutions. Estimating your project depends on many factors, such as what challenges your company is trying to solve, what artificial intelligence solutions, software, or tools would best serve your company, what your expectations are in terms of accuracy, the suitability of your data, and more.

For a more definitive answer, get in touch, and one of our experts will talk you through suitable machine learning services and give you an estimate based on an analysis of your precise requirements.

What machine learning services does Netguru offer?

Netguru offers a variety of services, from data collection strategy to building a scalable machine learning infrastructure.

AI Design Sprint – rapidly validate your machine learning project

ML Processes Audit – verify your machine learning delivery processes

Data Quality Assessment – plan your data collection strategy

ML-Ops Transformation – build a scalable machine learning infrastructure

Data-Ops Transformation – build a scalable data infrastructure

When should we use machine learning?

Machine learning applications can bring you more clients, provide greater insights, increase sales, and reduce business costs. However, if not used properly, they may lead to customer outflow, money loss, and reputation damage.

Data is the key to success in machine learning and deep learning applications. In traditional software development, humans create computer systems, and machines simply follow these pre-programmed rules. Thus, the crucial part of the application is the algorithm inside.

There are hundreds of business applications for machine learning solutions. In general, they solve several types of problems. The main ones are:

Classification: Is this credit card transaction fraudulent or not? Is this email spam or not? Machine learning tools are great when you need to divide objects (for example, clients or products) into two or more pre-defined groups.

Clustering: ML models are used to find parallels between data points and divide objects into similar groups (clusters). Importantly, there is no need to define the groups in advance.

Regression: It's like a future prediction. On the basis of an input from a data set (usually historical data plus other factors), ML models estimate the most likely numeric value of a particular quantity. It could be anything, such as stock prices, consumer behavior, or wear and tear on a piece of equipment.

Dimensionality reduction: In an ocean of information, ML tools can choose which data is the most significant and how it can be summarised. In practice, it is applied in such fields as photo processing and text analysis.

Although machine learning solutions give businesses numerous new options, there are situations when it's better to stick with traditional software methods.

When are you better off avoiding ML solutions?

You don't have enough data: machine learning is designed to work with huge amounts of data If the training data set is too small, then the system's decisions will be biased.

Data is too noisy: "Noise" in ML is the irrelevant information in a data set. If there is too much of it, the computer might memorize noise.

You don't have much time (or money): Custom ML solutions can be time- and resource-intensive. First, data scientists need to prepare a data set (if they don't do it, see point no. 2). Then, the computer needs some time to learn. Then the IT team performs a test and adjusts the model. Then, the computer needs some time to learn again. IT performs another test and adjusts the model. The computer goes back to learning. The cycle repeats over and over again. As the time needed increases, this is reflected in the pricing of your project.

You have a simple problem to solve.

To sum up: Machine learning models help find patterns in the chaos of big data sets. It is worth considering when you have a complex task to solve or if you’re dealing with a large volume of data and lots of variables. But this method has its limits. It's better not to choose it if you are limited by time or the amount or quality of available data.

Let’s work together

Looking for other services?


Check out the other services that we have in our range. We deliver high-quality products on time. Hassle-free.
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