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We support companies in the process of enabling and integrating technologies based on modern architectures to reduce costs and shorten delivery times.
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- Enabling containerization technologies in hybrid infrastructure.
- Deploying products on containerized platforms.
- Developing offerings on containerization platforms.
- Architecting and enabling DevOps workflows.
- Designing architecture and implementing Infrastructure as Code (IaC).
- Cloud service migration.
- Reducing operational and repetitive task execution time by up to 99%, such as backups, DevOps workflows, among others.
- Annual savings of up to 8% in cloud infrastructure billing.
- Reducing compliance risks, such as SOX, for example.
- Flexibility for standard deployments in hybrid infrastructure through IaC.
- Flexibility for standard configurations in hybrid infrastructure through CaC.
- Assessment of DevOps technical maturity.
- Technical support in DevOps adoption.
- Architecting and implementing CI/CD workflows.
- Administration and Support of community DevOps tools or Azure.
- DevOps pipeline as a service.
- Training.
- Monitoring of network and IT infrastructure components.
- Cost control by indexing data from other information systems.
- Financial optimization by reducing times in billing generation or work order cuts by integrating support documents into workflows.
- Greater efficiency in process verification or audits by integrating information systems with digitized third-party documents.
- Immediacy and security in document legalization processes by being able to sign remotely in a workflow.
- Reduced time by automating the receipt of electronic invoices.
- Digital generation of forms and their linkage in workflows for operational processes.
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We support organizations in achieving optimal states in their analytics processes by collecting, analyzing, and adding value to data, through building solutions to business problems that facilitate growth, efficiency, and innovation.
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- Data Engineering Services, focused on extracting data from multiple types of information sources
- Data Engineering Services, focused on centralizing and storing data.
- Data Engineering Services, focused on data transformation in terms of quality, enrichment, and data cleansing.
- Creation of reports and dashboards through descriptive analysis.
- Descriptive analytics solutions aimed at solving business problems and optimizing operations
- Transactional traceability of business processes in different information systems for compliance.
- Management visibility of capacities and resources by correlating their demand to project growth.
- Proactive monitoring of business operations.
- Capability for timely decisions based on transactional data in an agile, simple, and intuitive way, correlating different sources of information.
- Identification of atypical behaviors for a better understanding of business processes.
- Analysis of SAP security and IT operations
- Quality assurance of information to ensure compliance with regulations.
- Risk reduction in different information systems by visualizing their usage.
- Database audit.
- Demand planning and forecasting as a service.
- Reports and financial statements as a service.
- Creation of artificial intelligence-based bots.
- Creation of recommendation and classification models based on Machine Learning.
- Creation of predictive models based on Machine Learning.
- Analytical capabilities services
- Analytical capabilities services focused on security.
- Demand planning and forecasting as a service.
- Solution to integrate and centralize diverse data sources in organizations, consolidating them into a single source that provides reliable and timely information for decision-making.
- Cloud-oriented solution (AWS): This allows scaling resources according to the specific needs of the solution.
- Short implementation and deployment times, less than a week
- Single source of data: Aimed at consolidating a single source of information through the implementation of a Data Lake and a Data Warehouse.
- Saving money in the production process by optimizing the actual blend of raw materials and improving product quality.
- Compliance with quality regulations in production.
- Visibility of equipment consumption and wear.
- Monitoring the behavior of the most relevant equipment for the process.
- Acting in the physical process from the digital process based on the analytics of the collected data.