Enterprises looking for software to solve business problems and empower growth face a challenging dilemma: shall they invest in a fully custom software solution or go with an off-the-shelf product? At ENFINT we are convinced that in digital era unique value proposition should be powered by unique software solutions.

Our deep understanding of our clients’ business, their unique processes, challenges and needs, combined with our cloud-ready custom app development MSA platform, enables quicker time-to-market and lower cost for custom solutions, driving efficiency and success of our customers.

A strong mobile app adds value to your business by drawing in and retaining customers or optimizing internal processes. ENFINT mobile application development services are customized to suit your unique product through the delivery of native iOS, Android or cross-platform solutions.

However big or small your need, we’ll build you a beautiful mobile app that’s tailored specifically to your business needs. Our team of highly capable developers have a breadth of experience developing apps for many industries and can build any type of functionality for your app

The introduction of IT technologies into many fields of human activity has led to an explosive growth of the amount of available data. The financial sector, industry (IoT) and Internet services now require new approaches to data analysis.

We create solutions for data analysis and processing using Big Data technologies, offering new competitive advantages to our clients.

Our solutions are based on Apache Hadoop containing multiple libraries to process "big data" in clusters that may have multiple thousands of nodes.

We solve data processing tasks using the Apache Spark library featuring the maximum use of random-access memory, thereby ensuring high performance. Complex data processing algorithms are implemented using Spark SQL and Spark ML components.

In today's world, many tasks require real-time big data analysis in addition to conventional processing and storage. Our projects utilize Apache Kafka libraries to implement Data Injection /Data collection mechanisms, while Spark Streaming is used to implement a complex logic of data processing.

Access and data analysis are ensured by Apache Hive, which makes it possible to execute requests, aggregate and analyse data stored in Apache Hadoop, as well as provide users with a well-established SQL-based data processing tool.

Our work is based on using programming languages (Scala, Java, Python, etc.) and our proprietary accelerators to design data flows in Spark using a visual editor.

Artificial intelligence is becoming an essential part of modern IT systems, and machine learning models allow monetizing these organizations, creating new competitive advantages.

Our Data Science team specializes in creating machine learning and artificial intelligence models using the programming languages ​​and infrastructure R, Python, and Spark MLLib, and includes three centers of competencies.

The Competence Center for Risk Modeling and Treasury Optimization specializes in creating scoring models (credit and collection scoring), models of default probability (PD) and loss in case of default (LGD) used for IFRS9, as well as models used to build a dynamic balance Bank (profiles of outflow of deposits and early repayment, forecasting balances on current accounts, elasticity models and optimization models).

The Competency Center for Optimizing Operational Efficiency focuses on the development of anomaly search models, predictive maintenance equipment prediction, as well as solving a wide range of classification problems, including using NLP technologies, in order to increase the productivity of analysts or operational staff.

The Client Data Monetization Competency Center is developing models of the outflow and optimization of client communications that can be used in planning marketing campaigns and developing recommendation systems.

DevOps principles are utilized in the company's projects at all stages of their lifecycle. Development process is based on flexible methodologies and instruments ensuring transparency, as well as on architectural patterns providing easier scaling and implementation of updates.

Continuous-delivery conveyors are built using advanced IaC approaches, automated development and support processes, as well as configuration management. This provides a flexible, transparent development process that helps create highly fail-safe, advanced systems ready for rapid changes as per business requirements.

A significant role is played by building the Release Management automated process, which includes branch development management tasks as well as the management of project artifacts and release assemblies taking into account the specifics of the customer's processes and instruments.

Availability and disaster tolerance of the developed products and solutions (depending on their architecture) is reached by reducing the coupling between the components within applications, as well as by the separation of infrastructure elements.

MLOps or DevOps for Machine Learning allows combining the efforts of Data Scientists, Data Engineers and support specialists to increase the speed, quality of development and deployment of ML models.

This approach allows managing models, scaling their application to solve business problems and constantly monitor work quality metrics, identifying and eliminating signs of degradation in the early stages.

MLOps Tasks: reproducibility of training results for ML models by tracking data sets (samples), source code for creating models, model parameters, and self-trained models in a single model repository.

Using the infrastructure and services of public clouds enables enterprises to develop a number of important advantages:

• Reduced IT costs by eliminating the redundancy of the infrastructure used and optimizing tasks performed;
• Improvement of IT service availability through utilization of global infrastructure;    
• Ensuring business flexibility by transitioning from strategic planning of infrastructure to a more flexible planning system and automation of processes.

Gain the highest possible benefits with cloud-based technologies:

• Relocate your application to the cloud. Focus on your applications, not the infrastructure;   
• Optimize your infrastructure by using scalable platform services;
• Upgrade your applications through new approaches and application development and execution tools: Serverless Computing, Containers, DevOps, Cloud-Native Development;
• Transform (models, processes etc.) by implementing and applying new technologies such as ML, AI and data analytics;
• Build your processes of cloud infrastructure management and continuous cost optimization with ready-to-use automation script libraries on the Terraform platform.

The service-oriented architecture (SOA) is based on the interaction between the systems within the IT landscape using the ESB (Enterprise Service Bus) integration bus that provides the multiple use of business services and allows for making changes in processes and relationships between applications through configuring instead of programming. 

The microservice architecture facilitates the transition from large monolith solutions to a set of individual applications solving local tasks and interacting with each other or with other systems. The use of micro-services significantly reduces the commissioning periods for new functionalities, increases flexibility to make changes, allows for managing the downloads of individual components, and provides "on-the-fly" solution of scaling problems. 

Open API is used to provide user-friendly access to the customer's business processes and services for partners and clients, with an option to control and manage this access. The combined use of integration platforms, micro-services and Open API provides comprehensive end-to-end automation of interactions, from the customer's clients and partners to its internal information systems.