Artificial intelligence
Innovation takes shape with AI
Our main focus at Betacom is on the new Artificial Intelligence technologies that are revolutionizing the way we live, work, and communicate. Today, AI is no longer just a futuristic promise but a concrete reality that enhances business processes, optimizes resources, and improves the user experience.
The most advanced solutions include:
Machine Learning: Systems that learn from data to make autonomous decisions and improve over time.
Natural Language Processing (NLP): AI that understands, interprets, and generates human language, such as chatbots, virtual assistants, and automatic translators.
Computer Vision: The analysis and interpretation of images and videos by intelligent systems.
Deep Learning: Complex models inspired by the human brain, used for voice recognition, medical diagnostics, autonomous driving, and much more.
Generative AI: Technologies such as language and visual models that create original content in real-time.
These technologies enable the automation of complex processes, anomaly detection, behavior prediction, business decision support, and the creation of new growth opportunities.
Artificial Intelligence today represents a strategic tool capable of increasing efficiency, reducing costs, and driving innovation across every sector.
The application of AI across various sectors
Manufacturing / Industry 4.0-5.0: Many use cases in industry involve the field of computer vision. Examples include image analysis to improve workplace safety, identify issues in production processes, and detect risks for workers in the workplace, as well as defect detection in production. Machine learning algorithms are also frequently used, such as in predictive maintenance, to try to predict when certain machines will malfunction.
Fintech / Banking: In banking, computer vision algorithms are less commonly used; instead, machine learning and BI algorithms are typically employed to analyze banking data and extract insights about internal processes, customer segmentation, fraud detection, and credit risk evaluation. However, banking data can be very complex, which is why data engineering teams exist to make data accessible to other areas.
Insurtech / Insurance: In the insurance sector, there are numerous use cases, with machine learning algorithms commonly used for fraud detection and customer analysis. This approach helps identify potential suspicious cases and offer personalized policies to different customers. In this case as well, proper data management is crucial, making the role of the Data Engineer indispensable.
Real Estate: Based on a proprietary generative, predictive, prescriptive, and analytical algorithm. It includes a range of smart tools for real estate operators—unified and part of the all-in-one suite—that automate back-end activities and enable the generation of leads perfectly aligned with the properties.