We encountered information stored in spreadsheets of various formats, making security, operational efficiency, and reliable decision-making difficult.
The high number of employees involved in the project led to rework and the risk of inconsistent information.
We implemented an intelligent Data Science and Artificial Intelligence system, centralizing information, automating processes, and ensuring data management accuracy and security.
With advanced analytics and automation, the company improved operational efficiency, reduced costs, and began predicting market trends, enabling more strategic decision-making. The team, previously overwhelmed with manual tasks, can now focus on what truly matters: growth and innovation.
We identified opportunities to reduce delays and costs in the environmental study, which impacted the project’s progress when assessing ecologically significant cavities.
We implemented a predictive Machine Learning model (Random Forest) that analyzes historical data and estimates the environmental relevance of cavities from the early stages of the study.
Interactive maps: intuitive visualizations highlight critical areas with heat maps.
Cost Reduction: Accurate insights helped optimize infrastructure, preventing rework.
Decision-Making Agility: Evaluation time was reduced, enabling quick and assertive decisions.
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