Imagem gerada por IA AI-generated summary: The application of GenAI and Machine Learning in the healthcare sector is revolutionizing everything from patient experience to the operational efficiency of institutions. Process automation, improved diagnostics, and clinical decision support are just some of the innovations made possible by AI. However, for these solutions to be effective, a solid foundation in engineering and data analysis is essential. Imagine a patient who, upon leaving a medical appointment, immediately receives on their mobile device a clear and concise summary of the diagnosis and recommendations discussed, as well as prescriptions for medications to be purchased or scheduling of requested tests. At the same time, the doctor, relieved of administrative tasks, can dedicate more attention and empathy to the next patient. Scenarios like this are becoming a reality thanks to the application of Generative Artificial Intelligence (GenAI) and Machine Learning (ML) in the healthcare sector. The advancement of new technologies is redefining the healthcare sector, driving innovations ranging from the automation of administrative processes and patient interfaces to advanced data analysis for more accurate diagnoses and personalized treatments. Technologies such as Generative AI (GenAI) and Machine Learning (ML) are transforming how hospitals, clinics, doctors, nursing teams, laboratories, insurers, and managers make decisions, improving efficiency, reducing costs, and enhancing the patient experience. On the other hand, issues such as data governance, regulatory aspects, and compliance with the values of genuinely ethical practice raise concerns. In this dynamic scenario, understanding how these tools are shaping the healthcare field is essential for organizations and professionals seeking to offer faster, more accurate, and more accessible care. The importance of data engineering and data analytics. The effectiveness of GenAI and ML solutions depends directly on a solid foundation of data engineering and analysis. Accurate data collection, organization, and interpretation ensure that algorithms are trained with relevant, high-quality information, resulting in more efficient and reliable models. According to Gabriel Casara, CGO of BlueMetrics, it's crucial to understand that a good AI solution stems from a well-structured data framework. “Data is foundational. Models will work with it, and it's where we need to start. To use an analogy with the healthcare world, before we begin treatment, we need to have our pharmacy well-organized.” The revolution in patient experience and operational efficiency. The adoption of GenAI and ML in the healthcare sector is not just about technology, but a fundamental transformation in the patient experience and operational efficiency of healthcare institutions. These solutions enable the optimization of bureaucratic processes, the automation of repetitive tasks, and the delivery of personalized insights, benefiting both patients and healthcare professionals. Through the analysis of large volumes of clinical data, AI can reduce waiting times, improve diagnoses, and personalize treatments. Healthcare professionals also benefit, as they spend less time on administrative tasks and more time focused on direct patient interaction. Imagem gerada por IA AI in healthcare: discover some use cases Patient and healthcare professional engagement The interaction between patients and healthcare professionals is fundamental to the effectiveness of treatments. GenAI-based tools are transforming this dynamic by automating bureaucratic tasks and improving communication. For example, the Jiménez Díaz Foundation in Madrid implemented the "Mobility Scribe" AI system, developed by the Quirónsalud group. This system listens to conversations between doctors and patients, generates understandable medical reports, and proposes treatments that doctors validate. As a result, doctors can focus more on the patient during the consultation, increasing the quality of care. Diagnostic support Diagnostic Support Programs (DSPs) and Patient Support Programs (PSPs) play an essential role throughout the entire patient journey. These initiatives operate from the diagnostic phase to access and adherence to treatment, ensuring humanized and personalized care. The use of GenAI and ML in these programs allows for the automation of workflows, improved interaction quality, and a more fluid experience for patients and their caregivers. Through digitized care guidelines, it is possible to offer individualized support, ensuring greater treatment adherence and improving clinical outcomes. For example, Roche developed a digitized support program for patients with chronic diseases, using AI to personalize interactions and remind patients about treatment adherence. The program not only improved the patient experience by offering a continuous support channel, but also helped reduce treatment dropout rates, ensuring better clinical outcomes. Patient segmentation and sentiment analysis Understanding patients' emotions and perceptions is vital to providing personalized care. Machine learning algorithms can analyze patient feedback, segmenting it based on expressed feelings and specific needs. This allows healthcare institutions to tailor their approaches, improving satisfaction and clinical outcomes. The recent study “Probabilistic emotion and sentiment modelling of patient-reported experiences,” from the universities of Adelaide and James Cook in Australia, introduced an innovative methodology for modeling patient emotions from online narratives about their experiences. Using advanced topic modeling techniques, the researchers analyzed patient reports on the Care Opinion portal, identifying key emotional themes related to interactions between patients and healthcare professionals, as well as clinical outcomes. This probabilistic approach allowed for the prediction of emotions and feelings with high accuracy, providing valuable insights for personalizing care and improving healthcare services. Next Best Action (NBA) In the healthcare context, the Next Best Action approach uses AI to analyze real-time patient data, determining the most appropriate interventions at any given time. This allows healthcare professionals to offer personalized treatments, increase therapeutic effectiveness, and improve the patient experience. Although the following example belongs to the financial sector, its principle can be adapted to healthcare: EY implemented an Accounts Receivable Collection Assistant with AI technology that uses machine learning to prioritize accounts, identify at-risk clients, and recommend the next best course of action. In healthcare, similar systems could analyze patient data to suggest timely and personalized clinical interventions. Synthesis of medical literature The sheer volume of medical publications is growing exponentially, making it challenging for healthcare professionals to stay up-to-date. GenAI and ML tools can automate the analysis and synthesis of large volumes of medical literature, providing concise and relevant summaries that aid in informed clinical decision-making. MedSearch is an AI-based digital assistant developed by Arkangel AI, designed to streamline the search and analysis of medical literature in PubMed. It provides healthcare professionals with up-to-date and relevant information in an efficient and contextualized manner, improving the accuracy and speed of access to scientific evidence. Imagem gerada por IA Analysis of clinical trials Clinical trials generate a substantial amount of data that requires meticulous analysis to identify patterns and significant outcomes. Applying AI to this data can accelerate interpretation, improve the accuracy of results, and aid in the development of new treatments. The study "Accelerating Clinical Evidence Synthesis with Large Language Models" presented TrialMind, a GenAI-based platform that automates the systematic review of clinical studies. Using advanced language models, TrialMind improves the efficiency in identifying and extracting relevant data, facilitating the synthesis of clinical evidence for researchers and healthcare professionals. Support for clinical decision-making and care coordination. GenAI and ML tools assist healthcare professionals in making clinical decisions by analyzing large volumes of data to provide evidence-based recommendations. Furthermore, these technologies improve coordination among medical teams, ensuring that patients receive integrated and efficient care. The Cardiomentor project, for example, developed by Tecnalia and the Barcelona Supercomputing Center in collaboration with the Spanish Society of Cardiology, resulted in the first Spanish public application based on AI. Initially focused on providing general practitioners with easy access to up-to-date information on heart failure, the tool plans, in its second phase, to use anonymized patient data to improve its predictive and diagnostic capabilities. This aims to offer recommendations based on similar cases, optimizing patient treatment. Improved diagnostics and medical training GenAI and ML are revolutionizing medical education by providing interactive and personalized platforms for training healthcare professionals. These technologies enable realistic simulations and access to vast repositories of knowledge, facilitating continuous learning and updating in medical practices. The Son Llàtzer University Hospital in Mallorca has implemented an AI algorithm capable of predicting a sepsis diagnosis within 24 hours, with 96% accuracy. While the primary focus is improving patient care, the algorithm also serves as an educational tool for doctors and nurses, enhancing clinical training and understanding of the early signs of the condition. Meanwhile, the Doctor Balmis General Hospital in Alicante has implemented an AI-based image reading system for chest and bone X-rays, allowing it to detect pathologies with 90% accuracy. Automation of authorizations and improvements in customer experience. Administrative processes, such as prior authorization for medical procedures, can be time-consuming and prone to errors. Implementing GenAI and ML automates these tasks, reducing waiting times and increasing accuracy in treatment approvals, which benefits both patients and healthcare providers. Furthermore, the use of AI in patient care platforms, with agents capable of scheduling new appointments and exams, as well as chatbots able to access patient history and offer contextualized suggestions, are examples of new horizons opening up in the continuous improvement of the customer experience in healthcare services. Want to see   GenAI and Machine Learning   solutions   making a difference in your company? BlueMetrics' experience in projects for the healthcare epsistema. BlueMetrics has extensive experience in developing Data, GenAI, and Machine Learning solutions for companies in the healthcare ecosystem, helping to optimize operations, improve decision-making, and drive financial results. An example of this is the work done for a large American company in the sector, which mediates emergency services, such as ambulances, and patients' health plans. With the growth of its operation, the company needed more transparency and accuracy in its reports to meet regulatory and operational demands. To address this challenge, we developed a robust data infrastructure, implementing a data lake and a data warehouse, making reports more agile, detailed, and adaptable to new business rules. As a result, the company attracted four new payment processing systems, connecting hundreds of businesses and expanding its customer base. This is a case study in Data & Analytics, a foundational area for any Artificial Intelligence project. With a structured database, new functionalities using GenAI and Machine Learning are already planned in the roadmap, bringing even more efficiency and innovation to the sector. Conclusion: The advancement of Artificial Intelligence in healthcare is not just a distant promise, but a constantly evolving reality. From automating administrative processes to personalizing medical care, including the analysis of clinical trials and optimization of the patient journey, GenAI and Machine Learning are reshaping the sector. At the heart of this revolution are data engineering and data analytics – fundamental pillars for the development of reliable and scalable solutions. With proven expertise in GenAI, Machine Learning, and Data & Analytics projects, BlueMetrics accelerates the AI journey in healthcare ecosystem companies, ensuring a more efficient, innovative, and patient-centered future. Shall we talk about it? Learn about some Use Cases .

Imagem gerada por IA AI-generated summary: This article explores how technologies such as GenAI, Machine Learning, and data analytics are revolutionizing sports, especially soccer. These innovations are applied both to improve athlete performance—through physiological analysis, tactical strategies, and intelligent recruitment—and to enhance the fan experience, with personalized content, chatbots, and smart loyalty programs. Modern sport is undergoing a silent revolution, driven by technologies such as Generative Artificial Intelligence (GenAI), Machine Learning (ML), and advanced data analytics. Football clubs and sports associations that adopt these innovations are gaining a competitive edge, optimizing both the performance of their athletes and teams, as well as enhancing the experience of their fans and building loyalty among their members. In the following article, we will see concrete examples of these applications. GenAI, Machine Learning, and Data: Fundamental Concepts However, before exploring their specific applications in the sports context, it is important to understand what these technologies are: GenAI (Generative Artificial Intelligence) AI models capable of creating new content, such as text, images, and videos, from existing data. Machine Learning A subfield of AI that allows systems to learn and improve from data, identifying patterns and making predictions. Data Analysis The process of collecting, organizing, and interpreting large volumes of data to extract valuable insights. Applications in sports performance 1. Athlete performance analysis Using sensors in uniforms and high-definition cameras, it is possible to collect precise data on movements, speed, heart rate, and other physiological indicators. Through Machine Learning, this data is analyzed to identify patterns of performance, fatigue, and risk of injury, allowing for personalized adjustments in tactical training and guidance from medical departments. 2. Data-driven tactical strategies Analyzing data from previous match series, combined with machine learning technologies, allows coaches and analysts to develop more effective tactical strategies. It's possible to simulate different game scenarios and predict opponent behavior, optimizing decision-making during matches. 3. Intelligent player recruitment By analyzing large volumes of player performance data across various leagues, clubs can identify promising talents that fit their tactical and financial profiles. The use of Machine Learning facilitates the identification of patterns that indicate potential for success, even in lesser-known athletes from smaller leagues. Imagem gerada por IA Enhancing the fan experience Beyond improving the performance of athletes and teams and identifying opportunities in the player market, GenAI technologies, Machine Learning, and the intelligent use of data can be decisive in enhancing the experience of fans and members. Let's look at some examples: 1. Content customization Through the strategic use of data and GenAI, clubs can create personalized content for fans, such as news, videos, and messages based on their preferences and interaction history. This increases engagement and strengthens the emotional bond with the club. 2. Virtual assistants and chatbots By implementing GenAI-based chatbots, clubs offer 24/7 support for fans, answering questions about tickets, game times, and general information. These virtual assistants are constantly learning, improving the quality of service and providing increasingly helpful answers. 3. Smart loyalty programs By analyzing fan behavior data, clubs can develop personalized loyalty programs, offering rewards and benefits aligned with individual interests, increasing satisfaction and loyalty. Furthermore, through machine learning, it's possible to identify potential membership cancellations, triggering actions and promotions to prevent member loss. Imagem gerada por IA The strategic importance for clubs and associations The adoption of these technologies is much more than a trend: it's a strategic necessity in a market where customer demands tend to increase due to experiences on other platforms and applications, even outside the sports realm. Clubs that use GenAI, ML, and data analytics can therefore gain a number of competitive advantages: Making data-driven decisions reduces the risk of errors and increases operational efficiency. To improve athletic performance by identifying areas for improvement and preventing injuries. Increase revenue through more effective marketing strategies and personalized loyalty programs. Strengthen the relationship with fans by offering more engaging and personalized experiences. Want to see   GenAI and Machine Learning   solutions   making a difference in your company? Success stories in sports using GenAI, Machine Learning, and Data. In the first part of this article, we discussed how technologies such as GenAI, Machine Learning, and data analytics are revolutionizing sports, from athlete performance to the fan experience. Now, we will explore concrete cases that illustrate this transformation, highlighting initiatives from sports clubs and associations. Bundesliga: real-time commentary with GenAI The Bundesliga , Germany's top football league, in partnership with Amazon Web Services (AWS) , has developed a live commentary solution powered by Artificial Intelligence (AI). This technology uses generative AI models to produce automated, real-time commentary on match events in various languages and writing styles, such as "journalistic," "casual," or "Gen Z," enriching the experience for fans around the world. The implementation of this solution aims to improve fan engagement, especially for international fans, who can follow match updates in their native language and in a tone that resonates with their cultural preferences. Furthermore, the Bundesliga uses AI to generate metadata that enhances content discovery across its vast digital archive. This initiative is part of a broader Bundesliga strategy to become the most customer-focused league, using advanced technologies to deliver personalized, real-time experiences to fans around the world. LaLiga: Advanced statistics with AI LaLiga , the Spanish football league, has integrated AI into its core strategy, using predictive analytics and algorithms to enhance various aspects of the fan experience. Mediacoach is a match analysis platform that uses AI to provide detailed statistics and tactical insights. With over 3.5 million data points generated per game, the tool allows clubs to analyze player performance and make informed strategic decisions. All 42 clubs in the top two divisions have access to this platform, promoting uniform and in-depth match analysis. In collaboration with Microsoft, LaLiga launched the Beyond Stats project, which uses AI to offer fans statistics and analysis beyond traditional numbers. This initiative aims to provide a deeper understanding of the game, highlighting aspects such as tactical positioning and physical performance of the players. Recognizing the opportunities presented by the digital revolution, LaLiga created Sportian, a subsidiary dedicated to developing AI-based technological solutions for the sports sector. The company offers tools such as Calendar Selector, which uses algorithms to optimize match schedules, and Sunlight Broadcasting Planning, which simulates stadium lighting conditions to improve broadcasts. Finally, in partnership with the Royal Spanish Football Federation (RFEF), it is implementing AI in the refereeing system. The technology will be used to assist in the appointment of referees for matches and in the evaluation of their performance, with the aim of promoting greater transparency and efficiency in the process. Liverpool FC and DeepMind: tactics enhanced with AI. Liverpool FC collaborated with DeepMind to develop TacticAI, an AI tool that analyzes and suggests ideal positioning for corner kicks. The platform was trained using data from 7,176 Premier League corner kicks between 2020 and 2023, allowing it to identify patterns and suggest effective tactical adjustments. In blind tests conducted with Liverpool FC experts, TacticAI's suggestions were preferred 90% of the time compared to existing strategies, highlighting its effectiveness. According to Luciano Rocha, CCO of BlueMetrics, the use of AI and data by Brazilian sports clubs and associations offers enormous potential for growth and improvement: “We are the country of football and sports. We have a gigantic critical mass that can be immensely monetized, which therefore justifies more investment in this area.” Gabriel Casara, CGO of the company, adds: "We already have similar solutions in-house, and in many cases even better than these practical applications we've seen in sports. We have delivered over 190 successful projects for more than 90 clients, not only in Brazil, but also in the United States and the rest of Latin America. It's just a matter of adapting these solutions to the sports market with agility and efficiency." Conclusion: The application of GenAI, Machine Learning, and data analytics is redefining the sports landscape, creating consistent and significant competitive advantages for clubs and companies, improving on-field performance, and enriching the fan experience. For sports clubs and associations that want to stand out, investing in these technological innovations is no longer an option, but a strategic necessity, a survival imperative. It's time to turn the tide. Shall we talk about it? Learn about some Use Cases .

AI-generated summary: The first “AI in Practice” event, hosted by AWS and BlueMetrics at the Caldeira Institute and streamed live on YouTube, showcased the concrete application of generative artificial intelligence and machine learning in the corporate environment. Aimed at guests from the innovation ecosystem, the event brought together BlueMetrics experts to demonstrate how companies can transform hype into real results with rapidly implemented, high-impact projects. Six use cases developed for clients in Brazil and abroad were shared, along with discussions on data governance, technological choices, the risks of public AI, and adoption strategies. The event highlighted BlueMetrics' position as a leader in data and AI solutions, with nearly 200 projects delivered in a decade of operation. A morning of learning and exchanging experiences. Despite the low temperatures on the morning of July 3rd, the Campus Auditorium of the Caldeira Institute in Porto Alegre was packed for the "AI in Practice" event, promoted by AWS in partnership with BlueMetrics. Aimed at guests from the corporate ecosystem and also broadcast live on YouTube, the meeting aimed to present practical and strategic applications of generative artificial intelligence (GenAI) and machine learning (ML) in the business world. As the name suggests, the focus was less on concepts and more on rapid execution and the generation of real results, with concrete examples of projects implemented in different sectors. During the event, executives and experts from BlueMetrics demonstrated how robust data engineering, the use of AWS technologies, and agile development methods allow companies to implement AI solutions in just a few weeks, with a direct impact on operational efficiency, decision-making, and new digital experiences. Six real-world case studies developed for clients in the education, media, financial, and real estate sectors were presented, all illustrating how AI can quickly become a tangible competitive differentiator for companies. With ten years of experience, BlueMetrics has established itself as a leader in data analytics and, more recently, in generative AI projects. The company has already completed nearly 200 successful deliveries for approximately 100 clients in the United States, Brazil, and Latin America, combining technical excellence, pragmatism, and strong execution capabilities in complex data and artificial intelligence projects. Far beyond the hype: AI that delivers real results. Gabriel Casara, CGO of BlueMetrics, opened the “ AI in Practice” event by reinforcing the company's commitment to practical AI applications and its strong partnership with AWS. He presented market data that highlighted the consolidation of artificial intelligence as a transformative technology: according to him, 72% of global companies had already adopted some AI solution by 2024, and private investments reached US$ 252 billion. Casara highlighted that AI has surpassed the "hype" status and has begun to generate real value, with projections of an impact of up to US$15.7 trillion on the global economy by 2030. According to him, the disruption goes beyond the operational level and is already affecting strategic decisions, such as scenario analysis, product development, and even the creation of new business models. He also drew attention to the need to contextualize AI within the business environment. Casara said that while generic AI, used daily by individuals, is powerful, corporate solutions need to be adapted to the specific context of the business. He emphasized that the value of AI lies not only in the algorithms, but in how it is applied. Casara also emphasized the critical importance of data, since "good data generates good AI ." The executive stated that many AI projects end up requiring a restructuring of the company's data, because, even with a large volume of information available, the data is not always organized or ready to generate value. He compared the use of AI to a Formula 1 car: extremely powerful, but which does not generate results if driven on a poorly paved dirt road. This road, according to Casara, is the data, which needs to be well prepared for the AI to function efficiently. Gabriel Casara, CGO da BlueMetrics “ Price Waterhouse believes that we will have $15.7 trillion added to the economy by 2030 (with the AI revolution). The disruption is not just in operational tasks: it also affects the strategic issues of companies. ” The perfect storm for AI adoption. The following presentation, by Denis Pesa, CEO of BlueMetrics, began with a clear vision: artificial intelligence has ceased to be just a trend and has become a concrete tool for generating real value in business. Denis began his speech by presenting the company's new positioning, "data and AI solutions for the real world ," and reported that, in 2024, BlueMetrics took a strategic pause in its operations to thoroughly study the practical impacts of AI. The goal was to reposition the company to deliver applicable solutions with a quick return, going far beyond the hype generated by the subject. According to him, the great challenge was precisely to transform this hype into measurable results, something the company had already been achieving in dozens of projects with clear gains in productivity and revenue. Denis contextualized the current moment as a "perfect storm" for AI adoption: accessible cloud infrastructure (with AWS support), accelerated evolution of LLMs, data availability, and greater openness from companies to technological adoption. He presented examples of applications already in operation, such as intelligent virtual customer service, content generation, fraud detection, credit analysis, predictive maintenance, and personalization in e-commerce. An important highlight was the replacement of traditional dashboards with generative AI interfaces, which make data more accessible to managers and accelerate decision-making. The CEO also warned about the risks of the indiscriminate use of public AI in corporate contexts, such as data leaks, violations of the LGPD (Brazilian General Data Protection Law), model hallucinations, and reputational damage. He emphasized that companies need to invest in customized, secure, and contextualized models, avoiding known cases of misuse that have generated real losses. Denis concluded by emphasizing that AI should be treated as a strategic business agenda and encouraged companies to start with high-impact, low-cost MVPs, relying on the technical support offered by BlueMetrics and the financial support, in the form of MVP funding , offered through the partnership with AWS. He ended with an optimistic provocation: "The future is already here. Let's embark on this journey together." Denis Pesa, CEO da BlueMetrics “ The hype is now generating real results, financial results. That's a fact. We know this from the dozens, almost a hundred projects we've worked on in the last 18 months, implementing this technology for our clients. ” The increasingly strategic role of AI agents. In the third morning presentation, Fabiano Saffi, CTO of BlueMetrics, addressed the role of generative AI agents, which he described as " a hype within a hype ." He explained that agents are autonomous systems that act as an intermediary layer between the user and learning language models (LLMs), allowing these models to operate with greater assertiveness, security, and contextualization. Saffi highlighted that, unlike simple prompt -based interactions with generic AI, agents structure the decision-making process, integrate private company data, and generate more relevant and secure responses for the business. Saffi explained that the autonomy of agents means they can plan actions, break down complex problems into steps, use memory, access structured databases, trigger external APIs, and even interact with machine learning models. All of this is done in an orchestrated, secure, and controlled manner, through limits and validations. One example was an agent for the restaurant sector, capable of providing information about dishes, reserving tables, and automatically updating the menu without human intervention. He also highlighted a real-world application developed for an American client operating in the real estate investment trust (REIT) sector. In this project, BlueMetrics created multiple agents integrated with machine learning and generative AI models, capable of compiling financial data, analyzing scenarios, generating reports, and offering strategic insights to managers. According to Safi, this was possible thanks to the correct structuring of the agents, something that had not been achieved by other vendors who attempted solutions based solely on prompt engineering . In two and a half months, BlueMetrics delivered a robust solution, which is already in the final approval phase. Finally, the CTO of BlueMetrics presented the emerging concepts of " Generative BI " and " Generative Analytics ," which enable the automated creation of dashboards and analyses via natural language. He emphasized that these technologies, combined with intelligent agents, have great potential to improve engagement with data and transform how companies analyze indicators, develop diagnoses, and make medium- and long-term decisions. Fabiano Saffi, CTO da BlueMetrics “ If we provide the model with data in a structured way, in a secure environment, with compliance regarding our data, the response will be more complete. And that's one of the main reasons why we all started talking about agents. ” Three GenAI case studies, with deliveries made in record time. The following presentation, featuring case studies, was given by Diórgenes Eugênio (Dio), Head of GenAI at BlueMetrics. He shared three practical cases of operational transformation using generative AI, highlighting the speed and impact of these solutions. Dio began by reinforcing the objective of making the application of GenAI to businesses tangible, bringing concrete ideas that could stimulate the audience's creativity. The first case presented was from an educational group with a high demand for customer service during enrollment periods. For this client, BlueMetrics developed an intelligent virtual assistant that, using GenAI and multiple agents, is able to answer questions in a contextualized, scalable, and efficient way. The system uses filters to redirect sensitive topics, integrates with private databases, and enriches context, all within an architecture built in just 10 weeks. In the second case, Dio tackled a project with a major TV network focused on generating personalized content at scale. The client was struggling to transform news videos into textual content adapted for multiple platforms. The solution involved automatic transcription with AWS Transcribe, followed by unbiased summaries generated by GenAI. From this, specific texts were created for Instagram, Facebook, X/Twitter, and blogs, respecting tone, audience, and platform. The key differentiator was the use of multiple specialized templates for each channel, which increased the accuracy and personalization of the generated content. The entire pipeline was developed and delivered in six weeks. Finally, Dio presented a third case focused on the digitization and contextualization of historical print newspapers. The client possessed archives from the 1980s at risk of deterioration. After digitizing the material with AWS Textract, BlueMetrics applied OCR and pre-processing techniques to differentiate headlines from body text. GenAI organized the articles in a structured way, and then all the content was indexed in a vector database. This allowed for intelligent contextual searches, going beyond keywords and offering a much richer experience. The solution was created in eight weeks, even with high complexity and a high degree of technical creativity. In conclusion, Dio emphasized that these three projects—in education, media, and documentary archives—illustrate the power of generative AI to transform operations in weeks. He highlighted that the use of multiple agents with specific functions and accessible services, such as Amazon Bedrock, allows for the creation of robust solutions with speed and low cost, provided they are well-planned. Diórgenes Eugênio (Dio), Head de GenAI da BlueMetrics “ My goal here is that, by the end of this presentation, you will all be able to materialize some ideas, sharpen your creativity, and think about how you can bring this into your daily life, your business, your operation. ” Want to see   GenAI and Machine Learning   solutions   making a difference in your company? Next, three highly strategic case studies based on Machine Learning. After the coffee break , it was time for the presentation by Bernardo Trevizan, Head of Data & Analytics at BlueMetrics. He discussed the role of Machine Learning within the artificial intelligence ecosystem, distinguishing it from generative AI. He explained that ML is a subset of AI, focused on pattern detection and predictions based on historical data. Generative AI, such as LLMs (Large Language Models), is a product of ML focused on language, such as summarizing texts or generating content. Bernardo highlighted that many business challenges require models beyond language: for example, to predict default, detect fraud, or estimate future revenues. The first case presented was the optimization of credit granting in the real estate sector. BlueMetrics' client was performing this process manually, which generated inconsistency and risks. A classification model via Amazon SageMaker was implemented, which assigned ratings based on data such as income, marital status, and number of children. The model achieved 92% accuracy in predicting good payers and also allowed for customer segmentation for marketing campaigns, prioritizing those with a good payment record. Based on historical data, it is possible to predict that this solution will reduce delinquency by 46%. In the second case, Bernardo explained the development of a financial fraud detection model via Pix (Brazil's instant payment system), for a banking software company. Without prior data, an unsupervised ML model was chosen, capable of learning the standard behavior of individual and corporate accounts. When a new transaction was analyzed, the system checked if it was statistically outside the norm, and, if so, generated an alert of possible fraud. Using real-time inference in SageMaker, the model began delivering responses in less than 1 second, with the potential to block up to R$ 1.5 million in fraud. The third example was an integrated solution developed for the US real estate capital markets, combining data engineering, generative AI, and machine learning. The system allowed managers to request revenue forecasts for specific properties via natural language. The AI recognized the limitations of LLMs in predicting the future and triggered an agent with time series models in SageMaker, which returned estimates with a 5% margin of error. According to Bernardo, this solution replaced managers' intuition with unbiased analyses, accessible through a conversational interface. He concluded by emphasizing that machine learning is a continuous process that requires constant reassessment and training to maintain its accuracy over time. Bernardo Trevizan, Head de Data & Analytics da BlueMetrics “ Through machine learning, we were able to analyze customer profiles (good and bad payers). And we achieved 92% accuracy. In other words, every time the solution told me that a rating was A, I was 92% certain that it was, in fact, an A. ” The BlueMetrics way of delivering with agility and efficiency. In the sales area presentation, Gabriel Casara (CGO) and Luciano Rocha (CCO) presented BlueMetrics' strategic approach to data and artificial intelligence projects. Casara began by explaining that many companies arrive with the desire to implement AI, but still lack clarity about the real problem. BlueMetrics' role is precisely to help clients identify whether the challenge lies in the data structure, the application of machine learning, or the use of generative AI, and to act in an end-to-end manner , with the technical capacity to deliver from engineering to the final application. He emphasized that the company has a strong partnership with AWS, which provides the secure, scalable, and flexible foundation for the solutions, allowing BlueMetrics to be creative and agile even in complex projects. Luciano Rocha highlighted the importance of companies treating the use of AI as a strategic issue, not an improvised one. According to him, new generations of employees are already using AI naturally in their daily work, which increases the responsibility of leaders to structure these technologies within the organization safely and purposefully. He explained how BlueMetrics structured its business verticals, focusing on machine learning, generative AI, and data foundation, which has been the heart of the company since its inception. Luciano emphasized that behind every successful AI solution lies a well-structured database. Casara resumed speaking to present blue4AI , BlueMetrics' proprietary method based on four simple and practical steps: Discovery and design Proof of concept (PoC) Implementation (Deployment) Continuous optimization He explained that this model allows for rapid results in 6 to 10 weeks, with deliverables in the form of production-ready MVPs. The company has already applied this method to over 190 projects with a high degree of success, including for international clients. Casara concluded by reinforcing the company's competitive advantage in offering fast deliveries, end-to-end solutions, and high flexibility, whether by allocating entire teams or developing projects on demand, all with a focus on generating real and continuous value for clients. Luciano Rocha, CCO da BlueMetrics “ Our role here is to help clients look inward, to structure their data, to do so securely, and to accelerate these projects in a way that truly makes sense. ” Gabriel Casara, CGO da BlueMetrics “ We usually say, "There's no such thing as 'no,' right? Maybe there's 'not like this,' because we'll solve it in some other way. We have a lot of experience today, with over 190 implemented AI data projects and more than 95 satisfied clients. " Questions and answers and final considerations The question and answer session complemented the practical journey presented in the lectures. Diórgenes Eugênio (Dio) answered the first question, about how BlueMetrics chooses the best solution or technology for each project. He explained that it all starts with clarity about the business problem, and that the team segments the challenges into three areas: data, machine learning, or generative AI. Within each vertical, a technical evaluation is conducted that takes into account cost, quality, and latency, always focusing on the sustainability and scalability of the solution. Bernardo Trevizan added that the team's role as a technical consultant is reinforced, capable of translating high expectations into real and effective solutions, adapted to the data and infrastructure maturity of each client. The second question came from an industry representative who wanted to know how to apply machine learning in environments with data scattered across different systems (ERPs, CRMs, etc.). Bernardo responded by highlighting the importance of data engineering and the creation of a data lake , where all data is centralized and organized to support AI models. He explained that different algorithms are used depending on the nature of the problem (classification, regression, clustering) and that the process involves both science and experimentation. Furthermore, he emphasized that it's possible to adapt knowledge from one sector to another. For example, reusing credit rating models to categorize leads or emails. For industry clients, we recommend reading the case study we developed for a large truck and bus manufacturer, in which we were able to reduce process analysis time from 4 hours to 6 seconds. In the third question, regarding the use of data lakes , Bernardo and Fabiano Saffi explained that the choice depends on each objective: a data lake is ideal for exploring raw and varied data, while a data warehouse is geared towards structured analysis, generating KPIs and insights. In his closing remarks, Denis Pesa, CEO of BlueMetrics, shared a personal reflection on his journey. He recounted how for many years he felt frustrated because he saw value in technologies that were not yet recognized as essential in Brazil. Now, with the popularization of generative AI and its concrete application in the local market, Denis celebrated the moment as a true turning point: "first-world cutting-edge technology is finally within our reach . " Denis also highlighted that, upon seeing the presentation of some of BlueMetrics' most recent case studies, he realized that the company is not only replicating existing solutions, but, through its extensive technical expertise and multidisciplinary talent, is actually creating new and different solutions to better address the specific challenges of its clients. He concluded by thanking the team, AWS partners, and participants, and extended an invitation for companies not to let ideas stagnate and to begin transforming their challenges into real projects with technical, strategic, and financial support. Denis Pesa, CEO da BlueMetrics " Seeing these presentations we had here today, it's clear that we're managing to do different things, really new things. " This was our summary of the first AI in Practice event, presented by AWS and BlueMetrics. The full presentation can be accessed here. Did you like the case studies presented? Are you interested in applying technologies like GenAI and Machine Learning to solve your company's specific challenges? Let's talk about it. Learn about some Use Cases .

Imagem gerada por IA AI-generated summary: This article presents how data, artificial intelligence, GenAI, and machine learning are transforming the construction, architecture, and engineering industries, with a special focus on real estate developers. In the first part, we explore in a didactic way how these technologies work—from data collection and integration to the use of predictive models, computer vision, and generative AI to reduce costs, increase productivity, and improve the customer experience. In the second part, we present real-world case studies of companies in the AEC sector that already use AI in security, construction management, and documentation, as well as two cases from BlueMetrics: a US real estate management company that began predicting revenues with a margin of error of less than 5%, and a Brazilian real estate developer that reduced delinquency by up to 46% by automating credit granting. Fundamentals and innovations: how data, AI, and GenAI work in the real estate development industry. 1. Data as a basis (collection, integration and quality) Before any advanced application, it's essential to understand that data is the foundation of all digital transformation in the sector. Without consistent, integrated, and reliable data, no AI or Machine Learning solution can generate real value. In the context of real estate developers, we can have sources such as the following: Architectural and structural designs (BIM models) Data related to project scheduling, planning, and execution. Contract, supplier, and input management systems IoT sensors on the construction site (weather, humidity, temperature, vibration) Maintenance history and customer feedback. Building logs (water consumption, energy consumption, elevators) Advanced machine learning models require that this data be clean, aligned, and interoperable. In many cases, ETL/ELT pipelines are used for data processing, normalization, and enrichment, including with external data (weather, real estate market, regional patterns). Within the context of BIM (Building Information Modeling), the practice of coordinating different disciplines (architecture, structure, installations) in a digital model already generates a large volume of information. BIM is not only visual—it carries physical, chronological, and cost properties (3D, 4D, 5D) that can feed predictive models. With quality data, the path to AI and GenAI becomes viable. 2. Machine Learning and Generative AI Models Once the data is available and well-structured, it's possible to apply different artificial intelligence models. These models can predict future scenarios, automate complex processes, or even creatively generate new solutions with GenAI. With structured data, developers can apply different techniques: a) Predictive models Schedule forecasting and delays : identifying which stages are at risk of delay, based on historical productivity data, weather, and logistical bottlenecks. Cost estimation (dynamic budgeting) : predicting variations in inputs, adjustments to deadlines, and budgetary risk. Market demand/interest forecasting : within launches, use search behavior data, demographic profiles, and regional trends to project which product types will be in highest demand. b) Generative AI / GenAI Generative AI goes further: it "creates" new artifacts or simulations based on what it has learned from the data. Some applications: Automatic generation of floor plan or layout variants : offering multiple optimized configurations for sunlight, ventilation, views, or cost, in minutes. This transforms the design process. 4D construction scenario simulations : combining schedule + BIM model to project construction progress, anticipate logistical conflicts, equipment relocation, and interferences. Automated document and report production : preparation of memos, specifications, status reports, cost statements, and explanatory justifications for non-technical users. Generative AI can draft or create an initial version of these documents – reducing hours of manual work. c) Computer vision and image analysis On the construction site, cameras and drones can capture images of the work's progress. Computer vision models identify inconsistencies (execution deviations, non-conformities, safety issues) by comparing what has been built with the ideal design. It is also possible to recognize the presence of emerging structural flaws (cracks, deformations) and trigger immediate inspections. d) Hybrid models / “human-in-the-loop” Given the critical and regulated nature of construction projects, many systems use hybrid models: AI provides alerts or suggestions, but a technical team validates them before action is taken. This improves reliability and avoids "black boxes" that are viewed with suspicion in engineering. 3. Positive impacts: reduced costs, increased productivity, satisfaction. By combining well-processed data with robust AI models, the practical effects begin to appear. It's not just about technology, but about real gains for developers and their clients. Reducing rework and construction corrections : errors detected early through simulation or remote viewing prevent high rework costs. Better budget predictability and more reliable timelines : predictive models help reduce the risk of budget overruns. Increased productivity on the construction site : less time lost due to poor coordination, improved logistics for machinery and supplies. Improved customer experience : transparency in project progress, predictable delivery dates, and proactive maintenance. Faster and more informed decision-making : managers and directors can simulate "what happens if this step is delayed?" scenarios and adjust plans accordingly. Optimization of technical workflows : designers generate variants with AI; operational teams receive more precise instructions; suppliers plan deliveries in advance. Additionally, there is a strategic bias: while many real estate developers operate with tight margins and market risk, those who adopt AI and data will be better positioned to compete with lower operating costs and greater accuracy. Next, we'll look at some case studies from this segment, including two from BlueMetrics. Imagem gerada por IA Real-world examples of AI in the AEC/construction market Skanska Case: Security monitoring with AI Skanska, a global real estate development company, adopted an intelligent AI-powered monitoring solution to reduce theft, vandalism, and unauthorized access at large construction sites. For example, in the I-405 highway improvement project in the US, AI tools for active surveillance, video analytics, and remote response were implemented to make operations more proactive. Furthermore, Skanska developed Safety Sidekick in-house, a chatbot based on generative AI that assists workers and managers on the construction site: it answers safety queries, provides recommendations on site conditions, and helps with planning. And in collaboration with the Smartvid.io platform , Skanska uses computer vision to identify safety risks: photos and videos captured on the construction site are automatically tagged with potential violations (SmartTags) to alert the team. These mechanisms allow for anticipating incidents, reducing human risk, and accelerating responses, thus improving safety and operational efficiency. Špansko Case: Automating Construction Documentation with AI The European real estate developer Špansko adopted an AI tool to automate the daily production of construction reports from photos, images, and visual data captured on-site. The goal was to reduce the time inspectors spend drafting manual reports after returning to the office. The application allows the inspector to record photos and observations directly on the construction site. It then automatically generates a formatted report, already including images, notes, and a professional layout. AI assists in the visual integration (photos) with text comments, aligning everything into a consistent document. Observed results: Significant time reduction: the report was generated in minutes on-site, instead of hours in the office. Lower probability of human error and inconsistency in the document, because the layout and formatting are standardized. Greater efficiency in communication between construction teams and central offices, with rapid feedback and integrated visual data. These cases show that recognized companies in the AEC sector are already applying AI, computer vision, predictive models, and BIM integration to improve safety, construction control, and coordination. Next, we'll look at two BlueMetrics case studies in the real estate sector. Want to see GenAI and Machine Learning solutions making a difference in your company? Case Study: BlueMetrics 1: Revenue forecasts for a real estate management company in the US. Context A real estate asset management firm in the United States, part of a large corporate group, operated in a highly competitive and volatile market. To maintain a strategic advantage and predictability, it was crucial to accurately estimate future revenues. Prior to this project, the firm had already contracted BlueMetrics for data and analytics projects: building a structured database, information governance, and systems integration. This track record facilitated smoother collaboration in the subsequent phase. Problem Although the company had ample historical data and business intelligence tools, estimating future revenues for each property still relied heavily on the individual experience of managers. The process was manual, time-consuming, and subject to personal biases. With hundreds of properties distributed across different regions, multiple variables (occupancy, vacancy, seasonality, rent increases, operating expenses) needed to be considered. Simulating scenarios, comparing assets, and anticipating impacts was an operational and strategic bottleneck—especially in a rapidly changing economic market. Solution BlueMetrics has developed a hybrid solution that combines generative AI and machine learning models for automated revenue forecasting. The main interface is a conversational agent in natural language, where managers can type questions such as "What will the revenue of asset X be in the next 6 months?" or "If vacancy is 10%, what is the impact on portfolio Y?", without the need for complex dashboards or dependence on a technical team. Behind this agent, predictive models based on time series use the company's historical data (revenue, vacancy, costs) to generate estimates. The solution integrates with the manager's legacy systems, queries internal databases, and runs the models automatically. The architecture was built with scalable AWS services (such as Amazon Bedrock and Amazon SageMaker), ensuring security, performance, and easy scalability. Key differentiators: The combination of generative AI for interaction and ML for integrated prediction. A conversational interface that democratizes its use by non-technical managers. Native integration with existing systems and architecture using tools from the AWS ecosystem. Results The forecasts now have a margin of error of less than 5%, increasing the reliability of the plans. The subjectivity of estimates based on intuition has been eliminated, bringing more consistency to the process. Managers gained autonomy: simulations and consultations can be done directly through chat, without technical support. Financial planning has become more agile, with immediate and well-founded answers. The organization's analytical maturity has increased, and AI has ceased to be a one-off element, becoming an ongoing part of operations. With this solution, a Brazilian developer/manager in this segment could apply the same adapted model for projecting unit sales, condominium revenue, or return on investment for residential and commercial projects. Case Study BlueMetrics 2: Brazilian real estate developer optimizes credit granting with AI and reduces delinquency by 46%. Context One of the largest real estate developers in Brazil, in addition to selling residential units, also operates as a financing company, offering direct credit to buyers. This practice increases margins and competitiveness, but also significantly increases financial risk. With the growth in sales and the number of applications, the need to revise the credit granting model, which until then was manual and poorly standardized, became evident. Problem The credit analysis process relied on criteria that varied among analysts, often influenced by subjective factors. This led to inconsistent decisions, conflicts between sales and finance departments, delays in service, and difficulty scaling operations. In a real estate market with high default rates, the company needed an objective, agile, and reliable process to assess risks. Solution BlueMetrics developed a machine learning model trained on the developer's own historical data. The model considers variables such as income, marital status, and number of children to estimate the likelihood of default among new applicants. Integrated with an artificial intelligence agent, the system automatically generates a real-time risk score for each request, enabling fast, standardized, and data-driven decisions. The process remains under human supervision, but now with robust support from reliable predictions. The solution architecture was built using scalable AWS technologies, such as Amazon SageMaker, ensuring performance, flexibility, and security to support operational growth. Results A potential 46% reduction in default rates, according to simulations using historical data. 92% accuracy in classifying good payers. Standardization of credit analysis, eliminating subjectivity and internal conflicts. Greater agility in service, with automated decisions in real time. Additional insights for marketing to target campaigns to customers with a higher propensity to pay. With this solution, the developer achieved greater efficiency, scalability, and financial security, demonstrating how the combination of data, machine learning, and AWS technologies can transform critical processes in the real estate sector. Conclusion: AI and data are the foundation of the new era for real estate developers. The incorporation of data, artificial intelligence, and machine learning is no longer a distant trend in the construction, architecture, and engineering sector: it is a reality that redefines how developers plan, build, finance, and interact with their clients. From the use of generative models in design to revenue forecasting and credit analysis based on predictive algorithms, companies that adopt these technologies are gaining clear competitive advantages: lower costs, greater efficiency, faster decisions, and more satisfied customers. With over 200 AI and data projects delivered to more than 90 clients in Brazil, the United States, and Latin America, BlueMetrics has accumulated the experience and expertise necessary to transform initiatives into concrete results. Our strong partnership with AWS ensures that each solution is built on scalable, secure, and adaptable foundations. More than just implementing technology, our mission is to understand your company's specific context and develop tailored solutions, whether it's to reduce delinquency, increase revenue predictability, improve customer experience, or optimize operations on the construction site. The future of the construction industry is already underway, and those who get ahead on this journey will have a competitive edge. Let's talk about it? Learn about some Use Cases