Be ahead of competitors with Artificial Intelligence Solutions

Let technology do the work for you. Solve key business problems with AI-driven solutions.
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Improve your performance thanks to AI-powered applications

Artificial Intelligence (AI) provides cutting-edge solutions that help businesses solve problems, automate tasks, and serve their customers better. AI-based systems excel at tasks like suggesting products users may like, recognizing objects in pictures, and automating monotonous jobs to help people save time.


Airbnb enjoys a nearly 4% lift in booking conversion thanks to Artificial Intelligence

A way to improve product and marketing with AI algorithms

Airbnb has successfully used artificial intelligence to create new products, improve their service, and take advantage of new marketing strategies. In addition, they have leveraged machine learning to detect host preferences. For each search query that a guest enter, Airbnb’s model computes the likelihood that relevant hosts will want to accommodate the guest’s request. In their A/B testing the model showed about a 3.75% increase in booking conversion, resulting in many more matches on Airbnb.

What started as a small research project resulted in the development of a machine learning model that learns our hosts’ preferences for accommodation requests based on their past behavior. The model showed about a 3.75% increase in booking conversion, resulting in many more matches on Airbnb.

Bar Ifrach

Former Director of Data Science at Airbnb

Artificial Intelligence can be used across practically sectors and industries

Artificial Intelligence can support your business in many ways.
  • Saving

    Increase sales

    49% of customers are willing to purchase more frequently when Artificial Intelligence is present.

  • Rocket

    Improve productivity

    Artificial Intelligence technologies are projected to increase labor productivity by up to 40% by 2035.

  • Artificial-intelligence

    Analyse large volumes of data

    AI 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.

  • Love

    Improve customer satisfaction

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

Starting an Artificial Intelligence project is not easy. Choose the right solution

There is a variety of Artificial Intelligence solutions tailored for different business needs.
  • Rocket

    Data Engineering

    Prepare your data to make the most of AI algorithms.

  • Growth

    Data Science

    Find meaningful insights to improve your product or service.

  • Users

    Recommender Systems

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

  • Translation

    Natural Language Processing

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

  • Face-recognition

    Computer Vision

    Automate difficult decision-making processes based on images.

  • Speak

    Audio Recognition

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

Let’s work together

The right process is the key advantage

Over the years, our team has tested and implemented a transparent and efficient workflow for Artificial Intelligence projects. The process helps our customers receive more reproducible results faster and in a more flexible way.

Our workflow focuses on three stages:

How we work?

  1. Training the model and making sure it has everything it needs

  2. Creating a model capable of producing predictions

  3. Connecting the model to your application.

What is Artificial Intelligence?

Artificial Intelligence (AI) is a broad category that includes cutting-edge concepts such as deep learning. In general, AI is all about bringing aspects of intelligence to machines and having them perform tasks which can be natural and easy to humans, but extremely complicated to program. Moreover, an AI agent can execute such tasks autonomously and efficiently.

AI-driven solutions can be classified into two groups:  general and narrow. General AI (also called strong AI) is what data scientists aim to develop in the future. It would be designed to solve broadly-defined problems intelligently thanks to sophisticated cognitive abilities and general experiential understanding of its environment. Today, that might sound like a science fiction scenario - but someday it’ll become a reality.

But AI is already changing the world as we know it - in the form of narrow AI, which focuses on performing specific tasks with incredible performance, often better than humans. For example, Pinterest uses a narrow AI solution for tagging images on the platform.

What data can you use for building AI solutions and how to do it?

Developers who want to create an AI-powered solution need to train algorithms on a set of diverse data - for example, a collection of images, text, or specific information like financial transactions or products viewed by users.

You can buy prepackaged data, take advantage of public crowdsourcing initiatives (such as the Amazon Mechanical Turk) or - when dealing with potentially sensitive data - hire private crowds of data science specialists able to help you out in data collection, identification, and labeling.

The dataset used for training needs to include a sufficient number of both positive and negative examples to help algorithms learn from it. For instance, if we want our algorithm to identify ugly cranes on pictures, we need to show it pictures both with cranes and without them.

The developer or data scientist may experiment with different algorithms before deciding which one is the best fit for the training data. But that’s not everything. We also need to provide the developer with a test set, a dataset used to test the model developed on the basis of the training data for evaluation and improvement.

What is the future of Artificial Intelligence and Machine Learning?

According to Gartner, “Artificial Intelligence and Machine Learning have reached a critical tipping point and will increasingly augment and extend virtually every technology-enabled service, thing, or application.” They also predict that by 2020, AI will become one of the top five investment priorities for at least 30% of Chief Information Officers.

Consumers are now increasingly used to digital assistants, self-driving cars, robots working in factories, and smart cities. AI made its mark on most industry sectors and continues to spread to new industries.

Experts predict that Machine Learning will grow at an increasing rate. Some people believe in the inevitable rise of that technology offered as a cloud-based service - the so-called Machine Learning-as-a-Service (MLaaS).

Ultimately, Machine Learning will keep on helping our machines to make better sense of data; both its context and meaning. And these powerful and actionable insights will make AI-based solutions indispensable for data-driven decision making among executives and project managers of the future.

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 technologies and testing various solutions in our R&D department, sharing our experience both on expert blogs and at various tech conferences such as IGARSS, AAIA and MICCAI.

Our team has worked on different engagements, including many end-to-end projects.

Our partners about the cooperation 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, IKEA, PAYBACK, DAMAC, Merck, Volkswagen, Babbel, Santander, Keller Williams, and Hive.
  • Self-care mobile app that lets users practice gratitude


    $5M Granted in funding

  • Investment platform that enable to invest in private equity funds


    $28M Granted in funding

  • Data-driven SME lending platform provider


    $20M Granted in funding

  • Lead generation tool that helps travelers to make bookings


    $47M Granted in funding

Frequent questions asked by our clients.

Based on our experience and questions we receive from our clients, we wrote down a list of Frequently Asked Questions. We hope it will help you better understand the issues related to Machine Learning.
How to estimate an Artificial Intelligence project?

Enterprise software development projects are very difficult to estimate. Adding an Artificial Intelligence (AI) modules make them much more challenging. That is why you should keep in mind that you are asking engineers to write a program that will generate a program, which will learn to do something useful for your business. It is complicated. It's difficult to answer this question in few sentences that is why we've answered it on our blog.

What exact Artificial Intelligence 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 to use Artificial Intelligence

Artificial Intelligence applications can bring you more clients, 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 (or lack thereof) of AI applications. Short and simple: 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 of artificial intelligence. In general, it solves 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 is a great tool when you need to divide objects (for example clients or products) into two or more pre-defined groups.

Clustering: ML discovers patterns in chaos. It enables those who use it to find parallels between data points and divide objects into similar groups (clusters). What is important, there is no need to define the groups in advance.

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

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

Although AI gives businesses numerous new options, there are situations when it's better to stick with traditional software methods.

When are you better off avoiding AI?

You don't have enough data: Artificial Intelligence is designed to work with huge amounts of data. Really huge. 100k records is a good start. If the training data set is too small, then the system's decisions will be biased.

Data are too noisy: "Noise" in AI is the irrelevant information in a dataset. If there is too much of it, the computer might memorise noise. This was the case of Tay.

You don't have much time (and money): AI is time- and resource-intensive. First, data scientists need to prepare a dataset (if they don't do it, see point no. 2). Then, the computer needs some time to learn. Then the IT team performs test and adjusts the algorithm. Then, the computer needs some time to learn, again. IT does some testing, and adjusts the algorithm. The computer goes back to learning... The cycle repeats over and over again. The more time is needed, the more you need to pay IT specialists.

You have a simple problem to solve.

To sum up: Artificial Intelligence helps find patterns in the chaos of big datasets. 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

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