Open Source Development

Leveraging open-source frameworks for foundational technology development, ProCoigas enhances collaboration on industry-standard projects, yet distinctly demarcates its proprietary developments to ensure commercial advantage.

ProCoigas’s expertise in harnessing the collective power of open-source communities alongside a keen understanding of IP strategies to deliver cutting-edge, secure, and proprietary Data & AI solutions that drive industry standards and innovation.

Our data solutions are powered by the following technologies

R Services

Our mastery of R covers a wide spectrum of services, from Shiny development to RStudio – Posit environment installations and SAS to R conversion. Our team’s deep knowledge of both SAS and R ensures a seamless transition, harnessing R’s advanced data analytics and machine learning capabilities. We’re also specialists in crafting customized R and Shiny environments that align with your unique needs. With a partnership dating back to 2014, you can rely on our expertise to deliver end-to-end solutions that drive measurable results.

Python Services

Our proficiency in Python spans a broad array of services, from developing Django or Flask web applications to setting up sophisticated Python environments and converting legacy code to Python. Our team’s deep understanding of various programming languages ensures a smooth transition, leveraging Python’s superior data analytics, machine learning, and web development capabilities. We excel in tailoring Python environments and applications to meet your specific requirements.

Package Development Services

Package development in Python and R represents a crucial endeavor that bridges complex functionalities with user accessibility. Both ecosystems advocate for documentation, unit testing (with pytest for Python and testthat for R), and version control integration, ensuring that packages are well-documented, tested, and maintained. This dual-path development in Python and R enriches the respective communities, fostering innovation and efficiency in tackling diverse computational challenges.

Our Approach

  • We review and prepare SAS programs, caching input data for development and identifying critical return objects
  • Formalize a program style guide designed for portability and long-term maintainability
  • We have developed a series of tools for evaluating SAS output and verifying migrated code matches. These tools and test are bundled with all completed migrations so the client can verify the quality of the migration.

Open-Source Flexibility

  • R can be run on various platforms, including Windows, and is a proud partner with Linux, allowing users to switch between systems and utilize their preferred hardware configurations
  • Switching to an open-source R model is much more cost-effective in comparison to legacy models, saving companies thousands
  • Maximize existing investments through R’s ecosystem with SAS’s established infrastructure, enabling clients to leverage their existing SAS models and processes while harnessing R’s rich set of packages and tools.

Cost Savings

  • We leverage capabilities by utilizing specialized analytical functions and procedures from SAS alongside R’s advanced statistical and visualization capabilities, enabling clients to tackle complex problems effectively
  • Tailored solutions to specific needs by combining SAS’s enterprise-leveln integration capabilities with R’s customizable and extensible nature, allowing clients to adapt solutions to their unique requirements
  • Converting SAS code to R allows users to work with data from different file types (e.g. CVS, Excel, JSON) and databases (e.g. MySQL, PostgreSQL) seamlessly.

Efficient and Scalable

  • We’ll work closely with businesses to ensure a smooth and successful transition to R, ensuring minimal daily disruptions in the transition process
  • By taking advantage of our SAS to R conversion and training services, businesses can improve their data analysis processes and stay ahead of the competition
  • We keep our clients up-to-date with the latest developments in analytics through continuous innovation and sharing of information. Our aim is to educate them on the evolving landscape of analytics.

Our Solutions

Discover how our team of Data Engineering specialists can turn your data problems into data solutions.

The ProCogia Blueprint

At ProCogia, we collaborate with our clients throughout their projects, working closely to build a roadmap, understand their challenges and develop tailored solutions. We do this through discovery meetings using our data framework to identify data issues and create a project management plan.

Assess Current State​

Analysis of the organization's processes, data handling, and technology to identify inefficiencies, compliance issues, and risks. Fully understand the current state of operational, technological, and strategic preparedness for transformation, laying the groundwork for future steps.

Prioritize and Execute

Uses agile methods for responsive, iterative development aligned with the organization's needs. With thorough quality checks for code and tests, we ensure consistent reproducibility. ProCogia focuses on strategic priorities, resource efficiency, and continuous KPI alignment.

Define Objects & Metrics

Collaboration with stakeholders to align goals and optimize service parameters for efficiency, ensuring applications match user needs and support the organization's strategy. ProCogia will then focus on promoting data-driven decisions and setting key performance indicators (KPIs).

Develop Infrastructure and Skills

Improving data management for better decisions, addressing skill shortages with managed staff augmentation, and adding AI technologies. ProCogia specializes in integrating external talent and enhancing data practices to boost the organization's tech abilities and align with its strategic goals.


these FAQs serve as a foundational resource for navigating the complexities of open source in the Data & AI landscape, ensuring that individuals and organizations can make the most of open source opportunities while mitigating potential risks.
Open source software offers several advantages, including cost savings, flexibility, and a strong community support system. It fosters innovation by allowing developers to share, modify, and improve code. In the context of Data & AI, it accelerates the development of solutions by providing access to a wealth of libraries and frameworks that have been tested and refined by a global community.
Companies should conduct regular audits of the open source components used in their projects to ensure compliance with their licenses. Tools like FOSSA and Black Duck can automate the detection of open source licenses and vulnerabilities. Training for developers on the importance of compliance and the specifics of different licenses is also crucial.
Best practices include following the project’s contribution guidelines, writing clean and well-documented code, engaging constructively in community discussions, and submitting meaningful pull requests that address existing issues or add valuable features. It’s also important to be responsive to feedback during the review process.
Data privacy can be maintained by anonymizing datasets, using synthetic data where appropriate, and implementing robust access controls and encryption. Open source projects should adhere to data protection regulations such as GDPR and provide clear documentation on data handling practices to ensure transparency and trust.
Open source communities are instrumental in developing tools and frameworks that help identify, assess, and mitigate biases in AI models. The collaborative nature of open source allows for diverse perspectives in tackling ethical issues, leading to the creation of more fair and transparent AI systems. Engaging with these communities can provide valuable insights into best practices for ethical AI development.
Sustainability can be achieved through active community engagement, clear governance models, and finding a balance between open collaboration and commercial interests. This might include establishing a foundation, securing sponsorships, or adopting a dual licensing model. Ensuring the project remains relevant by continuously improving and adapting to the needs of its users also contributes to long-term sustainability.


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