Success Stories

1. Data Platform Modernization for a Retail Leader

The Challenge: A major retail chain with a national presence operated with an outdated data warehouse that generated reports days late. This slowness impeded agile decisions on inventory management, logistics, and marketing campaigns, resulting in losses due to excess stock and missed sales opportunities.

Our Solution:

* Strategy and Architecture: We led the design and implementation of a modern Big Data platform on Microsoft Azure. The legacy system was replaced with a Data Lake architecture, using Azure Data Factory for data ingestion and Azure Databricks for large-scale processing.

* Data Governance: A data governance framework based on the DAMA methodology was implemented to ensure the quality, consistency, and accessibility of information, identifying key business data sources.

* Real-Time Engineering: Our engineering team developed microservices in Python and Java that, using Apache Kafka, captured sales transactions in real time from points of sale. These services were deployed in Docker containers orchestrated with Kubernetes to ensure high availability and scalability.

* Data Visualization and Culture: APIs were created to connect the new platform with BI tools such as Power BI and Tableau, allowing product and marketing managers to access interactive, up-to-the-minute dashboards. We also led change management initiatives to foster a data-driven culture within the organization.

The Result:

90% reduction in report generation time, from days to minutes.

25% improvement in inventory turnover thanks to predictive demand analysis.

Ability to launch personalized and geolocalized marketing campaigns in real time, increasing conversion by 15%.

2. Creation of a Telemedicine Platform for a Healthcare Startup

The Challenge: A HealthTech startup needed to develop a secure, scalable platform from scratch that complied with strict international patient data management regulations. Time to market was critical to ensure a leadership position.

Our Solution:

* Leadership and Methodology: We assumed the role of Project Manager and Scrum Master, applying agile methodologies to manage the project. This enabled iterative development, continuous value delivery, and rapid adaptation to changes. Detailed project plans were developed and communication with all stakeholders was managed.

* Backend Architecture and Development: A microservices architecture was designed on Amazon Web Services (AWS) to ensure scalability and component decoupling. The backend was developed primarily in Java with Spring Boot, ensuring high performance and security. Our team's prior experience developing software for the healthcare sector, complying with German government regulations, was key to defining compliance requirements.

* Automation and CI/CD: A complete Continuous Integration and Deployment (CI/CD) pipeline was implemented with Jenkins and Docker. This automated testing (unit, integration, and performance) and deployments, drastically reducing manual errors and accelerating the delivery of new features.

The Result:

Successful launch of the platform in 6 months, 40% faster than initial projections.

The platform passed all security and compliance audits on the first attempt.

The scalable architecture supported user growth from 0 to 50,000 in the first year without performance degradation.

3. Optimizing Operations with Generative AI in the Banking Sector

The Challenge: A regional bank faced the challenge of validating a new data lake for risk. The process was manual, slow, and error-prone, with a metric acceptance rate by business users of just 9%. Business analysts, lacking technical expertise, were unable to exploit the wealth of available data.

Our Solution:

* Project Management: We led the initiative as PMO, focusing on removing impediments and energizing the team around the validation process. Work sprints and UATs (User Acceptance Tests) were executed effectively.

* Data Engineering and ETL: Existing data pipelines were optimized, and new ETL processes were developed using Python and PySpark to ensure quality and consistency.