Get ahead of the competition with Artificial Intelligence solutions

Let technology do the work for you. Solve key business problems with AI-driven solutions.

Improve your operations and business performance thanks to AI-powered applications

Artificial Intelligence (AI) provides cutting-edge solutions that can help your business solve problems, automate tasks, and serve your 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.

Increase sales

49% of customers are willing to purchase more frequently when AI is present.
Improve productivity

Artificial Intelligence technologies are projected to increase labor productivity by up to 40% by 2035.
Analyze large volumes of data

AI gives apps the ability to learn and improve over time. It is extremely adept at quickly identifying patterns and trends.
Improve customer satisfaction

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

Let’s work together

Man holding a mobile phone with airbnb app

Airbnb enjoys nearly a 4% lift in booking conversions thanks to AI solutions

Products and marketing improved with AI algorithms

Airbnb has successfully used AI to create new products, improve its service, and take advantage of new marketing strategies. In addition, it has leveraged machine learning to detect host preferences.

For each search query that a guest enters, Airbnb’s model computes the likelihood that relevant hosts will want to accommodate the guest’s request. In A/B testing, the model showed about a 3.75% increase in booking conversions, 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 conversions, resulting in many more matches on Airbnb.

$Bar Ifrach photo$

Bar Ifrach
Former Director of Data Science at Airbnb

Data Engineering

Prepare your data to make the most of AI algorithms
Data Science

Uncover meaningful insights to improve your products or services
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 identify 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

The right process is a key advantage

Over the years, our team has developed and implemented a robust and efficient workflow for AI projects. Our processes ensure our customers receive more reproducible results faster and in a more flexible way.

Our workflow focuses on three stages:

Training the model and making sure it has everything it needs
Creating a model capable of producing predictions
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 solutions are all about bringing aspects of intelligence to machines and having them perform tasks that can be natural and easy to humans, but extremely complicated to program. Moreover, an AI agent can execute such tasks autonomously and efficiently.

AI 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 will be designed to solve broadly-defined problems in an intelligent way 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.

AI-based software and tools are 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 software based on narrow AI for tagging images on its platform. And in the retail industry, AI-powered robots are being used to provide customer assistant services.

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

Developers who want to design AI solutions 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 with data collection, identification, and labeling services.

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 cranes in pictures, we need to show it pictures 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, analysis, and improvement.

What is the future of AI 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 the services of digital assistants, self-driving cars, robots working in factories, and smart cities. AI has 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 that it will inevitably be offered as a cloud-based service – 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 and analysis 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 solutions in our R&D department, sharing our experience both on expert blogs and at various tech industry conferences such as IGARSS, AAIA, and MICCAI.

Our team has worked on a variety of engagements, including many end-to-end projects.

Personalized shopping with Countr

Accurate product recommendations in a social shopping app

Countr is a personalized shopping app that enables its users to shop with their friends, receive trusted recommendations, showcase their style, and earn money for their taste – all in one place. Using machine learning, we delivered recommendation and feed-generation functionalities and improved the user search experience.

Read the case study

Speech-to-text transcription with CocoonWeaver

Take notes using nothing but your voice

We designed an intuitive UX and developed a neural network that, together with Siri, enables the app to perform speech-to-text transcription and accurately produce notes with correct grammar and punctuation.

Read the case study

Audio recognition with BabyGuard

Monitor your baby’s sleep remotely from another room

To achieve high performance, we used custom audio processing algorithms and neural networks to handle the classification of the signal. The system can detect a baby’s cry rapidly and accurately. Our designers handled the UX to make the app easy and intuitive to use.

Read the case study

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

15+

Years on the Market
600+

People on Board
1000+

Projects Delivered
73

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.

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AI services: All your questions answered

Not sure how AI solutions can deliver value to your business? Check out some of the most common questions asked by our clients.

How do you estimate an AI project?

It can be difficult to provide a ballpark figure for enterprise software development projects. Adding AI services makes it even more challenging. Estimating your project depends on many factors, such as what challenges your company is trying to solve, what AI solutions, software, or tools would best serve your company, what your expectations are in terms of accuracy, and more. For a more definitive answer, get in touch, and one of our experts will talk you through suitable AI solutions and give you an estimate based on an analysis of your precise requirements.

What AI 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 AI?

AI solutions can bring you more customers, increase sales, and reduce business costs. However, if not used properly, they could lead to an outflow of customers, money loss, and reputational damage.

Data is the key to success when implementing AI solutions. In traditional software development, humans create computer systems, and machines simply follow these pre-programmed rules. Thus, the crucial part of an application is the algorithm behind it.

There are hundreds of business applications for AI solutions. In general, they are used to develop software and tools that can help your company 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 is a great tool when you need to divide objects (for example, customers 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). 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 dataset, ML estimates the most likely numeric value of a particular quantity. It could be anything, such as stock or real estate prices, consumer behavior, or wear and tear on a piece of equipment within your company.

Dimensionality reduction: In an ocean of information, ML 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 AI solutions offer your company numerous new options, there are situations when it's better to stick with traditional software methods.

When are you better off avoiding AI solutions?

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

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

You don't have much time (and money): AI solutions can be 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 tests 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: AI 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.