As a systems integrator and solution provider, we offer services that cover the full life cycle of information systems: requirements, analysis, design, development, testing, implementation, and subsequent maintenance and evolution, always with the aim of achieving greater efficiency and productivity.
Within the framework of development and integration of Information Systems, Graviton Solutions encompasses different types:
- Business Applications. “Turnkey” Projects that cover the functionalities and the operational business processes in our clients.
- Migration or Conversion of Applications. Projects of technological evolution of applications that do not require substantial functional or operational reforms.
- Systems Integration or Applications. Integration projects between different solutions, Applications, Products that cover specific areas of process on the client.
This allows you to achieve the following objectives:
- Provide or define projects or services that will assist them to achieve the purposes of the Organization through the definition of a strategic framework for its development.
- Improve the productivity of the projects or services, allowing greater ability to adapt to the changes and taking into account the reuse as much as possible.
- Facilitate communication and understanding among the various participants in the production of software throughout the life cycle of the project, bearing in mind their role and responsibility, as well as the needs of each and every one of them.
- To facilitate the operation, maintenance and use of the software products obtained.
In the development of the methodology for the development and maintenance of Graviton Solutions, account has been taken of the most popular development methods, as well as the latest standards of software engineering and quality, in addition to specific references in regard to security and management of projects.
We also provide consulting services in the area of below cutting-edge technologies:
- IOT Development
- Artificial Intelligence (Supervised Learning/Un-supervised Learning) – Text Mining with Python’s NLTK
We use supervised learning as a technique to infer a function from training data. The training data consist of pairs of objects (typically vectors): a component of the pair is the input data and the other, the desired results. The output of the function can be a numeric value (as in the regression problems) or a label of class (as in the classification). The goal of supervised learning is to create a function capable of predicting the value corresponding to any valid input object after having seen a number of examples, the training data. To do this, you need to generalize on the basis of the data submitted to situations not seen previously.