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All Services

Web Design

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Marketing

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SEO

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Graphics Design

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UI/UX

"We Design seamless experiences for the modern world: Recognizing the significance of an appealing user interface and captivating user experience, we prioritize the design aspect of every application we develop. Our approach is comprehensive, taking into account all crucial elements, and our strategies are crafted to produce profitable outcomes Boxes
1)Research and Analysis : Our collaborative approach involves comprehending your business objectives and assessing the existing solution via multiple avenues, such as Google Analytics, feedback from clients, bug reports, analysis of competitors' solutions, and case studies
2)Design and Validation : Develop a detailed high-fidelity prototype comprising UI components, layouts, and information hierarchy, and continually validate it with end-users until achieving the optimal product and interface. 3)UI Implementation : We utilize Responsive Web Design best practices and provide cross-browser support while implementing the UI. In the case of complex enterprise systems, we adhere to a Design System to ensure consistency across all products"

Data Engineering

"Boxes :
1)Data Ingestion: Ingesting data from various sources such as databases, APIs, and files is a crucial step in data engineering. Tools such as Apache Kafka, Apache Nifi, and AWS Glue can be used for data ingestion.
2)Data Storage: Storing large volumes of data requires scalable and reliable solutions. Cloud-based storage solutions such as Amazon S3, Google Cloud Storage, and Azure Blob Storage can be utilized for data storage.
3)Data Processing: Processing data efficiently is critical in data engineering. Distributed processing frameworks such as Apache Spark and Apache Flink can be used for large-scale data processing.
4)Data Transformation: Transforming data into a suitable format for analysis is necessary. Tools such as Apache Airflow and AWS Step Functions can be used for data transformation.
5)Data Quality and Governance: Ensuring data quality and governance is essential for reliable analysis. Tools such as Trifacta and Talend can be used for data quality and governance.
6)Data Visualization: Visualizing data for better understanding is crucial. Tools such as Tableau, Power BI, and AWS QuickSight can be used for data visualization."

Java

"Boxes:
Object-oriented Programming: Java is an object-oriented language, which allows for efficient development and maintenance of large-scale applications.
Platform Independence: Java is platform-independent, meaning that it can run on any device or operating system that has a Java Virtual Machine installed.
Multithreading: Java supports multithreading, allowing for parallel execution of multiple tasks or processes.
Robustness and Security: Java is known for its robustness and security features, making it a popular choice for developing enterprise-level applications.
Integration with Libraries and Frameworks: Java has a vast ecosystem of libraries and frameworks that can be integrated into applications, such as Spring, Hibernate, and Apache Struts.
Performance Optimization: Java offers various performance optimization techniques such as just-in-time compilation and memory management, which helps in developing high-performance applications."

DevOps & Cloud

"Automate Your Overall Business Activities : With the aid of DevOps, you can accelerate your development cycle at a reasonable cost while boosting productivity swiftly. Don't hesitate any longer - choose SAS Tech Global managed services to expedite your business operations and achieve fruitful outcomes Boxes
1)Environment Configuration : To improve the efficiency of your current system, you can implement automated processes using Terraform or a deployment manager. Various tools such as CircleCI, AWS CodePipeline, GitHub Actions, GitLabCI, or Google CloudBuild can be utilized for CICD, although other options are also welcome.
2)Cloud Computing : relying on workload orchestrators such as ECS or Nomad. In addition, AWS Lambda, Cloud Functions, AWS Fargate, and API Gateway are utilized to enable effortless scaling and ensure high availability for utilizing serverless computing.
3)Container Orchestrators: By utilizing container orchestrators, you can enhance the flexibility and efficiency of your development process. Selleo offers the option to choose from popular container orchestrators like ECS, Nomad with Consul, or Kubernetes. Moreover, we can automate the deployment process for other workload tools as well.
4)Storage & Network Delivery : Leverage cloud storage and rapid content delivery to store and retrieve large volumes of data seamlessly from any location on the internet.
5)Cloud Databases : Leverage cloud storage and rapid content delivery to store and retrieve large volumes of data seamlessly from any location on the internet.
6) Application Integration : Set up SES, SNS, SQS, MQ, Athena and other AWS services, including more advanced such as Machine Learning."

Project Management

"Boxes: Task Management: Ability to create, assign, and track tasks and subtasks, set deadlines, and monitor progress. Resource Allocation: Tools for assigning and tracking resources, including personnel, equipment, and materials, to specific tasks or projects. Time Tracking: Ability to track time spent on individual tasks, calculate billable hours, and generate timesheets for payroll purposes. Collaboration and Communication: Features to facilitate communication and collaboration among team members, including messaging, file sharing, commenting, and real-time updates. Project Planning: Tools for creating project timelines, setting milestones, and tracking progress against project goals. Risk Management: Identification, assessment, and mitigation of risks associated with the project."

Mobile Development

"Boxes: Platform-specific development: Developers can use native tools such as Xcode for iOS and Android Studio for Android to create mobile applications that are optimized for each platform. Cross-platform development: Developers can use frameworks such as React Native, Flutter, or Xamarin to create mobile applications that can run on both iOS and Android devices with a single codebase. User interface design: Developers can design intuitive and visually appealing user interfaces using tools such as Sketch or Figma. App store deployment: Developers can prepare and publish their apps on Apple App Store and Google Play Store by following specific guidelines and requirements. Push notifications: Developers can set up push notifications for their mobile applications to keep users engaged and informed about new updates, promotions, or events. Mobile analytics: Developers can use analytics tools such as Google Analytics or Firebase Analytics to gather insights about their app's usage, performance, and user behavior."

Cyber Security

"Boxes: Vulnerability Scanning: Utilize automated tools to identify security vulnerabilities in applications and systems, and implement mitigation strategies. Security Information and Event Management (SIEM): Collect and analyze data from various sources to detect potential security threats and respond to incidents in real-time. Identity and Access Management (IAM): Implement policies and tools to control access to resources and ensure that only authorized users can access sensitive data and systems. Penetration Testing: Conduct simulated attacks on systems and applications to identify vulnerabilities and test the effectiveness of security measures. Encryption: Use encryption to protect sensitive data from unauthorized access or theft, and implement appropriate key management practices. Incident Response: Establish an incident response plan to quickly and effectively respond to security incidents and minimize damage. Compliance and Risk Management: Ensure compliance with relevant regulations and industry standards, and implement risk management strategies to minimize potential security threats."

AI / ML

"Boxes:
1)Data Preprocessing: Preparing data for machine learning algorithms is a crucial step. Various techniques such as data cleaning, normalization, and transformation can be employed to improve data quality.
2)Model Selection: Choosing the right model or combination of models for a particular task is critical. Machine learning algorithms such as linear regression, decision trees, and neural networks can be used for different tasks.
3)Hyperparameter Tuning: Tuning hyperparameters can significantly impact the performance of a model. Techniques such as grid search, random search, and Bayesian optimization can be used to find the best hyperparameters.
4)Model Training: Training models on large datasets requires significant computational resources. Cloud-based solutions such as AWS SageMaker, Google Cloud ML Engine, or Microsoft Azure Machine Learning can be utilized for scalable training.
5)Model Evaluation: Evaluating model performance is crucial to determine if it meets the desired accuracy and other metrics. Techniques such as cross-validation and confusion matrix analysis can be used for evaluation.
6)Model Deployment: Deploying machine learning models in production requires a robust infrastructure. Various deployment options such as Docker containers, serverless functions, and microservices can be employed for model deployment"

ETL

"Boxes:
Feature Engineering: Feature engineering involves selecting, transforming, and extracting the most relevant features from the raw data to improve the accuracy of machine learning models.
Data Augmentation: Data augmentation involves creating synthetic data from existing data to increase the size and diversity of the dataset, which can improve the performance of machine learning models.
Interpretability: Interpretability refers to the ability to understand and explain the decision-making process of a machine learning model. Techniques such as feature importance, partial dependence plots, and LIME can be used for interpretability.
Transfer Learning: Transfer learning involves using pre-trained models to improve the performance of a new model. This approach can be useful when there is limited data available or when training a model from scratch is time-consuming or computationally expensive.
Ensemble Learning: Ensemble learning involves combining multiple machine learning models to improve the overall performance. Techniques such as bagging, boosting, and stacking can be used for ensemble learning.
Explainability: Explainability refers to the ability to explain the output of a machine learning model in human-understandable terms. Techniques such as SHAP values, counterfactual analysis, and decision trees can be used for explainability."

125

Active Projects

200

Business Growth

530

Completed Projects

941

Happy Clients

What our customers say about us.

Lisa Gally
Project Manager

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Missy Limana
Project Manager

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Aana Brown
Project Manager

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Aana Brown
Project Manager

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