Tech

Modernizing Business with IT

Modern IT system development method for achieving “Digital Transformation”.

• DevOps & CD/CI (Continuous Delivery/Continuous Integration)

  Streamlining development through automated testing, integration, and deployment, enhancing collaboration and delivery speed.

• Cloud Native Computing

  Building and deploying applications that leverage cloud services for scalability, resilience, and efficiency.

• Agile Development Process

  Iterative and collaborative software development, emphasizing adaptability and customer feedback.

• Legacy System Modernization

  Transforming outdated systems to contemporary technologies, enhancing functionality, and extending lifespan.

• Modern Software Architecture

  Designing scalable, modular, and maintainable software structures using contemporary architectural patterns.

• Business Rule Extraction & Verification

  Identifying and validating key business rules for accurate system representation and compliance.

• Traceability Recovery

  Re-establishing links between software artifacts to enhance understanding, maintenance, and change management.

• Database Analysis

  Evaluating database structures and performance for optimized design and efficient data management.

Intelligent Systems Engineering

Technology for developing reliable, high-quality AI and AI-based systems.

• Quality Assurance for AI Systems

  Ensuring the reliability and effectiveness of artificial intelligence system through systematic testing, validation, and adherence to quality standards.

• Safety and Trustworthiness of AI

  Addressing concerns related to the safe and ethical deployment of AI, emphasizing transparency, accountability, and the minimization of biases.

• Ethics of AI

  Examining the ethical implications of AI technology, addressing issues such as privacy, bias, and the responsible use of intelligent systems.

• Development Process and Tools for AI

  Implementing structured methodologies and tools tailored for the efficient development and management of AI projects.

• Verification for Machine Learning

  Applying rigorous verification techniques to machine learning models, ensuring their correctness, robustness, and compliance with specifications.

• Data Generation for Learning & Testing

  Creating diverse and representative datasets to train and test machine learning models, enhancing their generalization and performance.

• Explainable AI

  Designing AI systems with interpretable models to enhance transparency, understanding, and trust in decision-making processes.

Data-Driven Software Engineering

Cutting-edge technology with big-data and AI for software development.

• System Development with Generative AI

  Using generative AI, radically enhancing the efficiency of the software development process from requirements definition, design, implementation, testing to development management.

• Project Monitoring and Alert

  Implementing systems for real-time monitoring of project metrics and generating alerts to ensure timely responses to potential issues.

• Program Analysis

  Evaluating and understanding program behavior through static and dynamic analysis, aiding in code comprehension and optimization.

• VR Source Code Visualization

  Utilizing virtual reality technology to visually represent and explore source code structures, facilitating a more immersive and intuitive understanding.

• Refactoring Recommendation

  Providing automated suggestions for code refactoring to improve maintainability, readability, and overall software quality.

• Performance Analysis

  Assessing and optimizing the efficiency and resource utilization of software systems to enhance overall performance.

• Fault Localization

  Identifying and isolating defects or errors in software, streamlining the debugging and correction process.

• Automatic Program Repair

  Implementing techniques to automatically identify and fix bugs in programs to enhancing reliability and maintainability.

• Optimized Testing Automation

  Streamlining the software testing process through efficient and effective automation strategies.

Safety System Development Process

Efficient process and method to develop safety-critical systems.

• Functional-Safety Compliance

  Adhering to functional safety standards and practices to mitigate and manage risks in safety-critical systems.

• Safety Case

  Developing a comprehensive safety case that articulates the safety measures and justifications for a system, particularly in safety-critical applications.

• Highly-Reliable Architecture

  Designing software architectures with a focus on high reliability, fault tolerance, and resilience to ensure continuous and dependable operation.

• Systems-Theoretic Risk Analysis

  Applying systems theory to analyze and manage risks in complex systems, particularly focusing on potential safety and security issues.

• Verification and Validation Process

  Employing systematic methods to verify and validate software, ensuring it meets specified requirements and quality standards.

• Log-Based Anomaly Detection

  Detecting abnormal behavior or potential issues in software systems by analyzing logs and other runtime data.

• Fault Injection

  Simulating faults or errors in a controlled environment to assess the resilience and robustness of a system under various failure scenarios.

• Keyword-Driven Testing

  Implementing testing strategies based on predefined keywords or actions, enhancing test case design and automation in software testing.