What We Do

Strategic planning
We can be the decisive factor behind your success

The first step is to define the scope of the solution and clearly identify the problem, which facilitates decision-making in later stages. Data quality is crucial, and sources must be carefully selected. It is important to determine the most appropriate type of AI solution. The choice of tools will depend on the programming language and deployment platforms. At this stage, the internal structure and interface are defined, and the design should be modular to allow for future improvements or adaptations.

  • Define specific need
  • Data types
  • Data Storage
  • Type AI Solution
  • Selection of Tools
  • Internal Architecture
  • Interface Architecture
Architecture Development

Once the architecture is defined, the agent is trained with the collected data. This process involves parameter configuration, variable adjustment, data exposure, testing, and validation of performance to measure the solution’s generalization and robustness. Integration and testing in a real environment: Before final deployment, tests are conducted in an environment that simulates real usage conditions. Proper execution of this phase helps prevent errors during deployment.

  • Architecture Development
  • Interface Development
  • Integration
  • Functional tests
  • Integration Tests
  • User Tests
  • Validation
Need & Solution

In the final phase, deployment takes place, which involves putting the solution into operation. The development of an artificial intelligence agent does not end with deployment. It is necessary to implement a continuous improvement cycle. Listen to users in order to identify areas for improvement or new needs. Systematically review results to prevent the agent from perpetuating or accentuating existing biases in the data. Finally, responsible AI development requires careful attention to social and regulatory aspects.

  • Deployments
  • Monitoring
  • Risk Analysis
  • Maintenance
  • Update
  • Ethics & Regulations