What we do
Use Cases
Descriptive Analysis
Renewable energy
A solar energy company analyzes the historical production data of its solar panels in different locations. The analysis shows how energy generation varies by season, geographic location, and weather conditions. This helps assess the performance of the installations and plan future investments in areas with greater generation potential.
Logistics
A transportation company reviews historical data on routes and delivery times to identify patterns in the performance of its vehicle fleets. The analysis reveals the most efficient routes and the times with the most delays, allowing the company to adjust its operations to improve punctuality and reduce operating costs.
E-Commerce
An e-commerce company examines historical data to identify which product categories had the highest sales volume, which days of the week had the most traffic, and how promotions influenced purchases. This information helps understand which products are more popular, which ones are underperforming, and how marketing campaigns affect sales, allowing for strategic adjustments in inventory and marketing to optimize future operations.
Predictive and Prescriptive Analysis
Pharma
A pharmaceutical company uses predictive analysis to estimate the future demand for a new medication. Based on historical sales data of similar drugs, clinical studies, and public health trends, the analysis predicts how many units of the medication will be needed in different regions, helping the company plan production and distribution efficiently.
Healthcare
A hospital employs predictive analysis to anticipate patient load in the intensive care units (ICUs). By analyzing historical admission data, seasonal factors, and disease patterns, the hospital can foresee periods of high demand and adjust resources and staffing accordingly, improving preparedness and emergency management.
Agriculture
A farmer uses predictive analysis to forecast crop yield based on historical climate data, soil types, and agricultural practices. This analysis allows for anticipating future harvests, optimizing fertilizer use, and adjusting irrigation strategies, helping to maximize yield and minimize risks associated with changes in weather conditions.
NLP and Generative AI
Aviation
Airlines use NLP (Natural Language Processing) to analyze comments and opinions from passengers on social media, surveys, and review platforms. By processing this text, patterns of satisfaction and areas for improvement are identified, such as the quality of onboard service or check-in efficiency, helping the airline enhance the customer experience.
Oil and Gas
In the oil and gas industry, NLP (Natural Language Processing) is used to analyze communications and technical reports in real time. This includes the automatic review of operational reports and communications between teams to detect potential issues or deviations in operations, such as leaks or equipment failures, facilitating a quick and effective response.
Automation
Automobile manufacturers implement NLP (Natural Language Processing) in chatbots and virtual assistants to provide customer support. These systems can answer questions about vehicle features, maintenance options, and warranty inquiries, offering immediate and personalized assistance to vehicle owners.
Geospatial Data Analysis
Real State
Real estate agencies use geospatial analysis to assess the location and value of properties. They analyze data from recent sales, market trends, proximity to services such as schools and shopping centers, and neighborhood characteristics to provide buying or selling recommendations and establish appropriate market prices.
Turism
Tourism companies use geospatial analysis to design and optimize tourist routes. By analyzing data on popular attractions, visitor density, and accessibility, they can create more efficient and appealing itineraries, enhance the tourist experience, and direct visitor flow to avoid congestion at points of interest.
Construction
In the construction industry, geospatial analysis helps plan and manage projects more efficiently. By integrating data on topography, land use, and existing infrastructure networks, builders can select the best sites for new projects, anticipate logistical challenges, and optimize the design and execution of construction works.