Machine Learning

Development Services

Machine learning technologies enable you to implement advanced data analytics for demand forecasting, fraud detection, risk scoring, and predictive maintenance. You can also use ML for personalized content recommendations, dynamic pricing, targeted marketing campaigns, chatbot and virtual assistant implementation, etc.

Yes, we can help you at any stage of a machine learning app development project, including data gathering and preparation. Our specialists will analyze what data can be used to train an accurate model and guide you through its cleaning and preparation.

Machine learning enables the implementation of medical image analysis in X-rays, MRIs, and CT scans. It also facilitates disease prediction, patient risk scoring, and personalized recommendations. In finance and banking, ML is used for fraud detection and risk scoring, while in retail, it enables dynamic pricing and custom product recommendations.

Yes, we offer legacy software optimization services that include ML implementation. We analyze your existing application and business goals to understand how machine learning can help meet them and assess whether the software allows for ML integration. Chatbot integration, speech recognition, and predictive analytics dashboards are some common use cases.

The project management approach depends on the type of cooperation. With team extension, remote software engineers join your team and work alongside your in-house developers. You assign them tasks, track their progress, and communicate with them. For the dedicated team model, we assign a PM who manages remote engineers and coordinates the work.

For machine learning development services, we provide team extension and dedicated team models. Team extension is suitable for projects that already have an in-house engineering team and need staffing more specialists. A dedicated team is an optimal choice for companies that want to outsource a whole project.

If we cooperate based on the team extension model, our engineers become a fully integrated part of your team. You interview pre-selected candidates with relevant tech skills and decide who to hire. They work remotely, but other than that, they follow your existing processes and complete the tasks you assign.

First, you must understand what business needs you have and possible ways to meet them. Then, you must evaluate your existing data and whether you have enough historical data related to the problem. Next, match ML capabilities with your use case to make sure machine learning is a feasible solution.

The budget for machine learning implementation depends on multiple factors, so we must learn more about your project to tell the cost. The scope and complexity of the problem to solve with machine learning, training data availability and quality, existing infrastructure, and required expertise are the main factors to consider.

Tell us about what you currently have, whether it's a product idea or legacy software that needs modernization. You can have a free consulting session with our team to learn what information we require in your case and optimize your efforts.