Machine Learning solutions for forward-thinking businesses

As a Machine Learning software company, Urja360 assists clients in enhancing products and making business decisions using state-of-the-art Machine Learning models

Professional software development and ML services

As a Machine Learning development company, we combine our expertise with software development, data analytics, visualisation and consulting support to ensure efficiency, safety and assistance to deliver end-to-end solutions. We create Machine Learning solutions using supervised, unsupervised and reinforcement learning to help you optimise your processes and enhance your products.

What we do – check out our Machine Learning services

Predictive Analytics

Predictive Analytics allows you to anticipate the future and make business decisions based on your current or historical data. It offers a variety of applications specific to a wide range of business sectors. Credit scoring can be applied to assess the likelihood of clients repaying loans. Sales forecasting is used to assess the demand for a product, while anomaly detection helps to identify risks and unexpected events with the use of data mining techniques. With the aid of predictive analytics, you can reduce risks while simultaneously improving business operations.

Churn Prediction

This is a part of predictive analytics that helps to answer a specific question: which customers end their relationship with a company (or stop using the product) and why? In other words, churn prediction lets you pinpoint when users are about to stop using your services before they do so. It becomes a massive asset when it comes to increasing customer retention. Thanks to churn prediction, you can learn what are the pain points related to your product or services, and find the right way to improve them.

Customer Analytics

Learning user behaviour and needs is crucial when it comes to digital businesses. Customer Analytics combines predictive analytics and customer segmentation to improve communication with customers and increase profitability as a result. We make it possible for companies to gain insights about the needs of a specific client segment and target tailored, direct marketing to their customers. When your selected user base is given the right message at the right time, you’ll see your conversion rates grow steadily.

Text Analytics

Text Analytics is used to translate large amounts of unstructured text into machine interpretable, quantitative data to speed up and automate all text-based processes. We can apply natural language processing methods to help you with all kinds of text, including documents, social media posts, surveys and chatbot conversations. One of the most popular applications of Text Analytics is automatic topic detection and sentiment analysis, which is particularly helpful when it comes to understanding your userbase’s needs.

Recommendation Systems

Personalisation is the key to success in the digital landscape, no matter which industry you operate in. Recommendation Systems powered by machine learning are used to predict user preferences based on their behaviour and experience. We apply recommendation engines to provide your customers or users with personalised content by suggesting the products and services they’re most interested in. By giving your users recommendations that are tailored to them, you can make sure to improve their satisfaction with the service, and increase overall sales.

Artificial Neural Networks (ANN) and Deep Learning (DL)

We harness most of the available Artificial Intelligence options to give you a solution you can be truly satisfied with. Neural Network-based solutions can find complex patterns in data that would otherwise remain hidden. We apply Artificial Neural Networks and Deep Learning solutions for image, character and speech recognition, where other Machine Learning methods are not applicable or efficient enough, in order to provide you with a seamless digital product that will leave your competitors far behind.

Tangible results, right on schedule

2 weeks

for the prototype

3 months

for the MVP

Need your idea verified fast?


years in remote software development


digital solutions delivered


of all projects conducted remotely

Custom Machine Learning solution development

Are you struggling to find a solution to solve your complex business questions? Choose our custom AI-based services. We combine Machine Learning software development with data analytics, visualisation and consulting support to provide you with comprehensive insights.What is there to gain? By exploring unexploited or previously unavailable analytical areas, we can help you to boost your business performance and stay ahead of the competitors. We will provide you with a complete solution, from assistance in goal setting and refinement all the way to visualisation and reporting solutions. Our fit-for-purpose approach leverages state-of-the-art methods and adapts them to make sure they do exactly what you need.

Want to know more about Machine Learning?

Does Machine Learning sound confusing to you? Don’t worry, pick a question and we will provide you with a brief answer!

Machine Learning (ML) automatically recognises complex, previously unknown and useful information in all types of data. In the ML process, a model learns by looking for patterns hidden within given data. The more data there is, the more accurately the model resembles the real process. Additionally, by adjusting model parameters we can further improve its performance. Having an adequate model built, we can then generalise its application and make predictions about fresh data.
There are two common types of ML tasks. Firstly, classification that predicts output based on given data. This can be used, for example, for a credit scoring or product dement prediction. The second one type clustering that assigns similar objects together, which makes it a perfect solution for customer or user segmentation.
This is an iterative process of building Machine Learning solutions in a pipeline. Generally speaking, it consists of the following steps: data wrangling, model building and making predictions. The model is a simplified representation of a modeled process. The data phase includes data collection, data exploration, feature engineering, and splitting data into training and evaluation sets. After that, models are built, evaluated, and their performance is improved by hyperparameter tuning. With the model deployed, we can make predictions using new data. The last phase in the project lifecycle is model maintenance and support.
Features or variables are measurable characteristics of the object (like a product or a customer) and are a basic input building block of all datasets. Based on them, prediction or clustering is performed. During the feature engineering process, new features can be obtained from raw features with the aim to improve an algorithm’s performance.
Natural Language Processing (NLP) is an automatic manipulation and understanding of written or spoken text. It intersects such fields as linguistics, artificial intelligence and machine learning.
Recommendation Systems are algorithms used for suggesting relevant content or products for users or shoppers. Recommendations are generated on user-item attribute similarities.
Predictive Analytics models in business applications use patterns found in historical data to identify future risks and opportunities. Apart from machine learning techniques, predictive analytics also encompasses statistics and data mining. One of its well-known applications is credit scoring used in the financial sector.
Deep Learning (DL) relates to a subgroup of Artificial Neural Networks (ANN) that have more than 3 layers and therefore can extract higher-level features. ANN are a part of Artificial Intelligence that solve complex and non-linear problems by mimicking animal neuron network behaviour. DL systems are self-teaching and able to filter information through multiple hidden layers in order to resemble human brain processes with an even higher accuracy. It is an implementation of artificial intelligence and can be used e.g. for automatic machine translation, image classification, voice recognition or self-driving cars.

Interested in starting a project with us?