Advanced automation in news transcription Reduced operational costs and increased efficiency Accuracy and impartiality in the generation of summaries AI-generated image AI-generated summary: One of Brazil’s largest television networks faced challenges in transcribing and summarizing news reports, impacting operational efficiency and multiplatform content distribution. In partnership with BlueMetrics, the broadcaster implemented a Generative AI-based solution using AWS Transcribe and Bedrock to automate the conversion of audio to text and generate unbiased summaries. Overview The client is one of the largest television networks in Brazil, with a significant national presence through more than a hundred stations, both owned and affiliated. With a strong presence in investigative journalism and police-related programs, the network stands out for its intensive coverage of criminal cases and investigative stories. In a market of accelerated consumption of digital news, the client was looking for a solution that would increase its operational efficiency, guarantee the impartiality of the content and optimize the multiplatform distribution of the reports. Market context: Need to transform audiovisual content into text Growing demand for accessible and structured digital content Agility in providing journalistic information Evolution of digital journalism and the need for editorial standardization Problem: How to make news processing more efficient? The manual process of transcribing and summarizing audiovisual content required considerable resources and extensive time, impacting the agility in the multiplatform distribution of content and its subsequent use in other journalistic productions. According to Gabriel Casara, CGO at BlueMetrics, "This is a very common pain point in organizations that need to deal with large volumes of unstructured data, in this case, content. But it also applies to a wide variety of companies, in segments as diverse as Education, Health, and Finance." Main challenges: Operational limitations: The manual transcription process requires exclusive dedication from professionals Long time between content production and its availability in text format Inconsistency in sthe tandardization of abstracts due to different writing styles Difficulty in maintaining a standard of impartiality in transcripts and summaries Business limitations: Underutilization of journalistic collections due to a lack of structured documentation High operational costs with dedicated transcription staff Delays in making content available on other platforms Difficulty in scaling content production for different channels Technological limitations: Lack of an automated solution for large-scale audio processing Lack of tools that guarantee neutrality and impartiality in content summarization Complexity in integrating different media formats and systems Need for specialized processing for the specific language of police journalism The Solution: Generative AI for automated transcription and summarization AI-generated image BlueMetrics has developed an automated audiovisual content transcription and summarization solution, using AWS cloud services to process criminal reports and generate neutral and impartial summaries, maintaining journalistic integrity. According to Diórgenes Eugênio, Head of Gen AI at BlueMetrics, “In addition to the complexity of transcribing audiovisual content with high accuracy, we had to ensure that the summaries generated did not express any bias or opinion. This was the biggest challenge, as even small details could compromise the required neutrality. We used AWS Transcribe to convert the audio to text. After transcription, we moved on to the second component: generating unbiased summaries. Here, the challenge was to ensure that the model did not infer subjective interpretations or draw conclusions about the facts. We used AWS Bedrock to integrate fact-based summarization techniques. The combination of Transcribe, Bedrock, and our custom validation layer was essential to deliver a pipeline aligned with the broadcaster’s requirements.” Main Components: Automated audio-to-text processing system Abstract generation engine with neutrality control Scalable asynchronous processing pipeline Structured database for storage and consultation Automated data input and output flow Technological Differentials: Using specialized AI to transcribe audio into Portuguese Use of generative AI in the summarization of extracted texts Serverless architecture with on-demand processing Native integration with existing infrastructure Immediate Benefits: Significant reduction in content processing time Standardization in the quality of generated summaries Guarantee of neutrality and impartiality Automatic scalability on demand Optimization of operational resources Faster availability of content in text format Want to see   GenAI and Machine Learning   solutions   making a difference in your company? Results: The implementation of the automated solution had a direct impact on the productivity of the client’s editorial team, significantly reducing the time required to transcribe and summarize criminal reports. As a result, the broadcaster began to make its content available in multiple formats more quickly and efficiently, meeting the growing demand of the digital market. In addition, the adoption of AI ensured a higher level of standardization and impartiality, factors essential for the credibility of investigative journalism. By optimizing the workflow, the solution allowed journalists to focus on strategic activities instead of repetitive tasks, increasing the capacity to produce new content. The structuring and organization of the extracted data also facilitated the reuse of journalistic material, increasing the value of the broadcaster's archive. Operational Efficiency: Complete automation of the transcription process Significant reduction in content processing time Freeing up teams for strategic activities Standardization in reporting documentation Editorial Quality: Guarantee of neutrality and impartiality in summaries Consistency in the language and format of the generated texts Greater accuracy in documenting criminal cases Maintenance of the broadcaster's journalistic standards Content Management: Structured documentation of the journalistic collection Ease of retrieving historical information Better organization of the material produced Agility in multiplatform distribution Technologies used The solution was designed using several AWS technologies, including: AWS Services Transcribe Bedrock S3 Lambda DynamoDB Cognito Amplify Languages, Libs, and Frameworks Python Javascript Node React Conclusion: The implementation of this solution significantly transformed the client's ability to process and make available its journalistic content, establishing new standards of efficiency and quality in the Brazilian television market. The project not only optimized operational processes but also opened up new possibilities for monetizing and leveraging the broadcaster's rich journalistic collection. For Gabriel Casara, CGO of BlueMetrics, this project once again demonstrates our company’s ability to develop solutions that bring real impact to businesses. “This is a solution that has enormous potential for application in companies that generate large volumes of information, such as newspapers, radio stations, communication networks in general, and content or streaming platforms, to name just a few examples.” How about creating a case like this for your company? Let's schedule a call . Discover some Use Cases . About BlueMetrics BlueMetrics was founded in 2016 and has already delivered more than 160 successful solutions in the areas of Data & Analytics, GenAI and Machine Learning for more than 70 companies in the United States, Brazil, Argentina, Colombia, and Mexico. It has its own methodology and a multidisciplinary team focused on delivering solutions to real challenges in the business world.

AI-generated image AI-generated summary: Personalizing user experiences with Generative Artificial Intelligence (GenAI) and Machine Learning (ML) has become a key competitive differentiator for companies across a range of industries. Data quality and structure are essential for AI models to deliver accurate interactions, efficient predictions, and highly personalized experiences. Industries such as healthcare, education, retail, finance, and hospitality are already using generative AI to optimize operations, improve customer service, and drive automated decision-making. In recent years, and increasingly, personalizing the user experience has become a crucial competitive differentiator for companies using Generative Artificial Intelligence (GenAI) and Machine Learning (ML). At the heart of this transformation is one essential element: data. The quality, structure, and volume of data determine the effectiveness of an AI model in delivering personalized experiences, accurate predictions, and contextual interactions. According to a McKinsey study, “Data cannot be an afterthought in generative AI. It is the essential fuel for a company to extract real value from technology.” The importance of data for GenAI and Machine Learning models GenAI and ML models intrinsically depend on data quality to: Create more accurate and coherent responses The richer and more well-structured the dataset, the better the model understands the context of interactions and provides more relevant suggestions. Personalize experiences in real time Models trained with contextual data can tailor content and recommendations to each user’s specific needs. Enhance automated decision making High-quality data allows AI to anticipate demands and propose solutions autonomously and efficiently. Real-world examples of AI personalization AI-generated image The application of generative AI to personalize experiences is already a reality in several sectors. Here are some practical examples: Health Sector Jimenez Diaz Foundation In Madrid, the foundation has implemented the AI system “Mobility Scribe”, developed by the Quirónsalud hospital group. This system listens to conversations between doctors and patients, generates understandable medical reports, and suggests treatments that doctors must validate. The initiative aims to reduce administrative tasks for healthcare professionals, allowing them to dedicate more time and attention to patients. SalienceAI This startup focuses on developing and deploying AI specifically for the pharmaceutical industry, with an emphasis on data security and compliance with regulations such as HIPAA. Its algorithms are designed to perform well on biomedical data, helping to analyze and interpret complex information to offer personalized treatments. Education Sector AI-Powered Educational Platforms Educational institutions are integrating generative AI to create personalized content that adapts to each student’s pace and learning style. For example, intelligent tutoring systems analyze individual performance and provide tailored study materials and exercises, improving efficiency and engagement in the educational process. Educational Virtual Assistants Tools such as educational chatbots are being used to answer students' questions in real time, offering ongoing support outside of class hours and contributing to a more interactive and personalized learning experience. Here at BlueMetrics, we have developed several educational platforms and assistants for some of the biggest players in the Brazilian market. If you want to know more about this, get in touch . Online Retail Personalizing Shopping Experiences E-commerce companies are using generative AI to analyze customer buying behavior and preferences, offering highly personalized product recommendations. This approach not only improves the user experience but also increases conversion rates and loyalty. Virtual Shopping Assistants E-commerce platforms are implementing AI-based virtual assistants that interact with customers, answering questions and helping them choose products, making the purchasing process more intuitive and personalized. In the e-commerce sector, we have a very interesting case that involves Product Augmentation and Recommendation solutions. Discover it . Banking Sector BV Bank In collaboration with Accenture and Google Cloud, Banco BV has implemented the “GenCore” project, which uses generative AI to create hyper-personalized interactions with customers. During the trial period, the technology accelerated the creation of communications by up to 80% and increased the degree of personalization by 100 times, offering services aligned to the individual needs of customers. BBVA The Spanish bank has launched the conversational assistant “Blue”, developed in partnership with OpenAI. This assistant, integrated into BBVA’s mobile app, offers more than 120 functionalities, allowing customers to manage accounts and cards in a personalized and efficient way. Commonwealth Bank The Australian bank has embraced AI to enhance customer service, deploying intelligent chatbots that respond to queries in a personalized manner and in real time. This initiative has resulted in a significant reduction in the need for additional call center staff and improved operational efficiency. Here at BlueMetrics, we have a success story involving the use of AI to process customer documentation for one of the largest banks in Brazil. Check it out. Hotel Sector HotelPlanner.com The hotel booking platform has deployed AI-powered travel agents that can handle customer inquiries in a surprisingly human-like manner. These agents, trained on data from millions of recorded calls, can converse in 15 different languages, providing personalized recommendations, quoting prices, and processing payments. In their first month of operation, the AI agents handled 40,000 inquiries and processed nearly $200,000 in room reservations. HiJiffy The Portuguese startup uses generative AI to transform the guest experience in the hospitality industry. Its automated solutions enable hotels to offer personalized interactions in real time, answering questions, making recommendations, and resolving issues efficiently, significantly improving customer satisfaction. Hotelverse This startup has developed a platform that allows customers to select specific rooms through digital twins, providing an immersive and personalized experience. The technology has already been adopted by major hotel chains, such as Hyatt Hotels and Radisson Hotel Group, standing out for its innovation in personalizing the guest experience. At BlueMetrics, we serve one of the largest resort chains in Mexico. After helping them structure their data pipeline, we are now developing solutions that use all this information to provide better experiences for their guests and customers. Want to see   GenAI and Machine Learning   solutions   making a difference in your company? The BlueMetrics difference: expertise in complex data and AI projects At BlueMetrics, we understand that a well-structured data foundation is essential for effective GenAI and ML solutions. Our track record proves our ability to tackle complex data and AI projects, applying proprietary methods and cutting-edge technologies to transform raw data into valuable insights and actionable intelligence. Gabriel Casara, CGO of BlueMetrics, highlights: Gabriel Casara, CGO at BlueMetrics, highlights the importance of a well-structured database for the success of AI solutions. “Our data expertise allows us to develop GenAI and Machine Learning solutions that go far beyond automation. We create models that truly add value to the business, personalizing experiences and optimizing operations in a scalable way.” With over 160 projects delivered for over 70 clients in the US and Latin America, ranging from large corporations to innovative startups, our experience is broad and diverse. In addition, our practical, entrepreneurial, and results-oriented approach ensures that the data, GenAI, and Machine Learning solutions we develop add real value to businesses, providing fast and measurable results. Denis Pesa, CEO at BlueMetrics, reinforces the strategic impact of intelligent data usage. “Companies that master the use of data are the ones that will be at the forefront of the market in the coming years. Investing in GenAI and Machine Learning without a solid approach to data is like trying to build a building without a foundation. Our role at BlueMetrics is to ensure that this foundation is robust, reliable and capable of driving real results.” GenAI and ML as drivers of business growth strategy Investing in GenAI and Machine Learning projects is not just a matter of technological innovation, but an essential strategy for companies seeking to grow and differentiate themselves in the market. With increasing competition in several sectors, the adoption of generative AI can become a decisive factor in boosting operational efficiency, personalizing customer interactions, and exploring new business opportunities. Strategic advantages Companies that incorporate generative AI into their operations experience benefits that go beyond traditional automation. Some of the key differentiators include: Improving Customer Experience GenAI enables highly personalized interactions by adapting to each user’s specific history, preferences, and needs. Advanced chatbots, virtual assistants, and hyper-personalized recommendations are examples of how AI can enhance customer relationships. Intelligent Process Automation Unlike conventional systems, generative AI can learn from large volumes of data and optimize workflows, reducing costs and increasing productivity. This is particularly useful in industries such as customer service, finance, marketing, and supply chain. Custom Content Generation Media, advertising, and e-commerce companies already use AI to create personalized texts, images, and videos on a large scale, ensuring greater engagement and efficiency in communication with consumers. Data-Driven Decision Making With insights extracted from structured and unstructured data, generative AI helps predict market trends, analyze risks, and create more assertive business strategies. Scalability and Efficiency Well-trained AI models can handle an increasing volume of data and transactions without compromising the quality of analysis or service delivery. This allows businesses to scale without proportionally increasing their operational costs. The Role of BlueMetrics in Digital Transformation At BlueMetrics, we combine our data expertise with cutting-edge technology to deliver solutions that make a difference. Our projects are developed to meet the specific needs of each client, ensuring that GenAI and Machine Learning are used as efficiently and strategically as possible. Our commitment is to transform data into valuable insights that drive business growth, providing a real competitive advantage in the market. With an approach focused on innovation and scalability, we help companies across all sectors to adopt AI strategically, maximizing results and optimizing operations. If your company is looking for customization, scalability, and efficiency, contact us and find out how we can transform your data into a competitive advantage. Conclusion Personalization through generative AI represents a promising frontier for companies across a variety of industries. However, the success of these initiatives depends directly on the quality and structure of the data used. With a solid approach and experienced partners, it is possible to transform data into personalized experiences that delight customers, optimize operations, and drive business growth. Companies that invest in the combination of GenAI, Machine Learning, and data not only improve their operational efficiency but also create new business models and position themselves at the forefront of innovation. To stand out in the digital economy, it is essential to invest in AI with a strategic and results-oriented vision – and that is exactly what we want to talk to you about. Let's talk about it. Discover some Use Cases .

Or: AI without blah blah blah Gabriel Casara, CGO BlueMetrics AI-generated image AI-generated summary: In this article, Gabriel Casara explains in a simple and accessible way how Generative AI works, comparing it to a giant sponge that learns from everything that is publicly available on the internet, recognizes patterns and responds based on mathematical vectors. He shows how this “digital mind” — trained with billions of sentences — becomes truly useful when it is adjusted with human curation, specific language and internal company data, through techniques such as RAG. In the end, he argues that AI should be treated not as magic, but as a practical strategic tool. Barbecue talk When I say that I work with Artificial Intelligence, there is always someone who asks: “But how does this thing actually work?”. I understand a little. And since I learned without coming from a technical background, I will explain here as if I were at a barbecue with friends, as I would like to receive the explanation. It all starts with a kind of infinite sponge. Imagine a baby's brain with an absurd capacity to absorb information, but instead of taking years to learn to say “daddy,” it starts absorbing everything that humanity has written on the internet: books, news, recipes, forums, everything (as long as it's public, okay?). This sponge doesn't understand content the way we do — it doesn't know what “love” is, but it realizes that “I love you” is usually followed by “my dear.” What it actually does is recognize patterns. But this sponge doesn't stop there. In addition to absorption, it has a gigantic processing capacity. It's almost as if we combined the curiosity of a baby with the calculating power of a supercomputer. And that's how what some call a language model is born — I prefer to think of it as the embryo of a digital Einstein. Training Einstein This “Einstein” is trained with billions of sentences and words, and is constantly challenged to guess the next word based on the previous ones. If you get it wrong, adjust the weights. If you get it right, you get positive reinforcement (in technical terms, of course). This happens millions of times until it becomes a master at predicting and putting together sentences based on what it has learned. And it does this very quickly. But calm down, he doesn't think like us. He has no conscience, no critical sense, no opinion. But he learned so much, so quickly, and so well, that it seems like he thinks. It seems like he knows more than we do. Sometimes, he really does know. Other times, he's just tripping — the so-called "hallucination." And there’s more: his brain doesn’t work with words, but with vectors, which are numbers. Everything is transformed into a kind of “mathematical map of language.” This allows him to compare ideas, contexts, and expressions in a mathematical way. That’s why he understands that “car” and “automobile” are close, but “bar” and “tent” don’t make sense in the same vector, even though they seem to start the same way. Then comes the fine-tuning. This is when they take this already powerful Einstein. This is where curation and guidance (prompts) come in: “speak like a seasoned lawyer who is an expert in…”, “write like a poet”, “explain like a quantum physics teacher to a five-year-old”, etc. This process is overseen by humans who show what an acceptable response is. It is not yet intelligence in the philosophical sense, but it is beginning to look a lot like contextual expertise. AI-generated image Now, if you want this Einstein to be truly useful, you need to give him specific knowledge and new disciplines. That's where RAG (Retrieval-Augmented Generation) comes in. Think of it this way: Einstein knows everything... until 2023. But he doesn't know what's in your company's contract, internal regulations, or credit policy. With RAG, you give him a "cheat sheet": a document base that he can consult before responding. Then he responds with context. Then he becomes a true intelligent assistant. By the way, it's no coincidence that mine is called Albert. But when he gets confused in his answer, I call him Alfred — nothing against Batman's butler, as Alfred makes mistakes with more class. Want to see   GenAI and Machine Learning   solutions   making a difference in your company? AI is not magic When you put all this together — the absorbing sponge, the calculating brain, the human curation, and the right context — what you end up with is a digital Einstein trained for your business. In practice, this goes far beyond generating a pretty little text. You can create assistants that know everything about your business, automate complex processes, give information superpowers to your sales, service, legal, or education teams, and help them make faster, more informed decisions. But to do that, you need to stop seeing AI as magic and start treating it as a strategic tool. And before you hire yet another consultant to complicate what should be simple, I suggest you do the following: Log into your favorite LLM (I won't tell you where I created Albert — yet), feed it your information, your questions and your data. See what it returns. After that, if you want to turn this into something practical, real , and useful in your company — no blah blah blah — Talk to us. We'll set everything up with you quickly, efficiently, and explained like we're chatting at a barbecue. Gabriel Casara is CGO at BlueMetrics and passionate about barbecue with friends and AI.

From operational tool to strategic voice: how artificial intelligence is beginning to take over the space of consulting firms and influence decisions at the highest corporate level. Gabriel Casara, CGO of BlueMetrics Imagem gerada por IA AI-generated summary: This article discusses how artificial intelligence is moving beyond being merely an operational tool—focused on automating tasks, reports, and basic analyses—to becoming a strategic voice within companies, capable of influencing decisions at the C-level and board of directors. Functioning similarly to traditional consultancies, but with greater scale and speed, AI is already generating diagnoses, simulations, and strategic recommendations comparable to or superior to those of human analysts. Studies by the Harvard Business Review and McKinsey show that, in quantitative scenarios, AI can even surpass human leaders in efficiency, although it still depends on executives to create truly disruptive strategies. The future points to hybrid boards, where AI agents and human leadership work together, and the competitive advantage will lie in how to integrate these intelligences for faster, more accurate, and transformative decisions. AI is climbing the ranks. How far will it go? The corporate use of artificial intelligence is, so far, largely operational. Automating tasks, speeding up reports, summarizing meetings, generating documents in seconds. More recently, some tools are also beginning to assist in scenario analysis and projections. But the disruption that is looming is more profound: it can scale from the factory floor to the manager's desk, the C-level executive, and even the boards of directors. This is where a provocation arises. AI, applied at the strategic level, works exactly like consulting firms have always worked—only better. The classic consulting model is based on collecting facts and data, analyzing historical series, assembling frameworks, and building strategic models from past business cases. This is precisely what large language models, such as GPTs, do: they recombine existing data, test hypotheses, identify patterns, and offer consistent recommendations. But on a scale and at a speed that no army of human analysts could replicate. What is the purpose of consulting services today? The Economist article, “Who needs Accenture in the age of AI?” (June 26, 2025), describes this dilemma clearly. While for decades companies like Accenture thrived by translating complexity into strategy, today the very logic of that business is at risk. Why outsource diagnostics and action plans when internal systems can already generate comparable—and often superior—analyses? When strategic intelligence becomes part of the corporate infrastructure itself, the intermediary becomes superfluous. This is not just theory. A Harvard Business Review article, published in 2024, argues that in several typical CEO functions—such as product portfolio decisions or capital allocation—AI already performs more efficiently than human leaders, especially in highly quantitative contexts (HBR, “AI Can (Mostly) Outperform Human CEOs” ). Similarly, McKinsey points out that AI is transforming the very practice of strategic development, enabling faster diagnoses, sophisticated simulations, and a reduction in the human biases that often distort choices (McKinsey, “How AI is transforming strategy development” ). Still, there is a boundary to be considered. LLMs are trained on past data. Their power lies in recombining what has already been seen, not in inventing what has never been tried. This means that, while AI is capable of projecting scenarios based on facts and evidence, the creation of genuinely new strategies—those that break established patterns and open up new markets—remains dependent on human leadership. A false dilemma It's plausible to imagine that future boards will be hybrid: composed of both AI agents—specialized in finance, risk, market, or operations—and human executives capable of translating these diagnoses into business insights. In this scenario, the competitive advantage will not lie in choosing between humans or machines, but in how to integrate the two for faster, more informed, and strategic decisions. AI has already discovered the boardroom. The question for companies now is not whether it will have a seat at the table, but how—and in what roles. The organizations that answer this question first will likely set the pace for the next great wave of business competitiveness. Gabriel Casara is CGO at BlueMetrics and believes in the value of intelligence, whether artificial or not. Want to see   GenAI and Machine Learning   solutions   making a difference in your company?

AI projects only generate results when they are well-structured. The rest is just a workaround. Gabriel Casara, CGO of BlueMetrics Imagem gerada por IA AI-generated summary: The article discusses the concept of the workslop, presented by the Harvard Business Review, and shows how it threatens not only productivity but also trust and collaboration in companies. The central criticism is clear: without strategy, solid data, and governance, AI tends to generate more problems than solutions. Based on the experience of over 200 delivered projects, BlueMetrics argues that the path to extracting real value from AI lies in the combination of robust data engineering, business acumen, and a proprietary method (blue4AI) that guarantees ROI, predictability, and measurable results. In recent days, the term "workslop" has gained traction following an article in the Harvard Business Review. The word describes an increasingly common phenomenon: work produced by AI tools that appears sophisticated but is fundamentally generic, empty, and of little use. Lengthy reports, elegant presentations, or well-formatted analyses that, in practice, offer no substance and do not aid in decision-making. The impacts of workslops go beyond rework. The HBR article shows that this type of output directly affects trust and collaboration among team members: those who receive empty content tend to see the sender as less creative, less trustworthy, and even less competent. Furthermore, there are significant financial losses, since each instance of a workslop can consume hours of rework and cost millions in wasted productivity when considered on an organizational scale. The problem, therefore, is not just one of operational efficiency, but also of culture and strategy. A controversial study by the MIT Media Lab reinforces this concern: 95% of companies do not report a measurable return on their AI investments. It is important to note that this report has methodological limitations and is being debated in academia. Even so, it echoes a reality we have been observing: many AI initiatives fail not because of the technology itself, but because of how they are implemented. The HBR article also points to ways to reduce the workslop problem. Among them are the role of leaders in modeling the intentional use of AI, defining clear quality standards, promoting a "pilot mindset" that combines initiative with optimism about AI's potential, and reinforcing the idea of creative collaboration. In short, AI should be treated as a tool to enhance results and not as a shortcut to skip reasoning or execution steps. Our CTO, Fabiano Saffi, comments: “Often, analyses generated by GenAI are beautiful but generic. The problem is that they don't answer critical business questions or indicate what should be done. Even worse is when those who generate this content can't review it and think it's good enough.” This is the crux of the problem. AI doesn't replace strategy, it doesn't replace business knowledge, and it can't do all the work on its own. When used without a method, AI may seem productive, but it ends up creating rework, frustration, and hidden costs. Another significant risk is relying on generic AI platforms that haven't been trained with language models tailored to an organization's context. In this scenario, responses can be decontextualized and, in even more serious cases, the AI can hallucinate, inventing facts or data that compromise strategic decisions. Common examples include market recommendations based on outdated information, analyses that ignore critical industry variables, or even the creation of unrealistic metrics that sound plausible but have no basis. At BlueMetrics, our commitment is precisely this: to deliver AI solutions that generate concrete, measurable results in the short term. To achieve this, we combine structured data with advanced technologies such as GenAI and Machine Learning, always paying attention to the context, business strategy, and ROI of each client. Want to see   GenAI and Machine Learning   solutions   making a difference in your company? What really works At BlueMetrics, we've already delivered over 200 AI and data projects to more than 90 clients. What we've learned along this journey is simple: for AI to generate real value, each project needs to be very well structured. One of our biggest differentiators is our expertise in data engineering: it's not enough to simply connect an AI platform to any database. We build pipelines that guarantee contextualized, reliable data, ready to feed intelligent solutions. This is what separates generic analyses from truly actionable recommendations. Furthermore, strategy comes before technology. Thanks to our portfolio of solutions already delivered, we clearly understand the objectives of each project, the expected ROI, and the success indicators. This understanding prevents AI from becoming just an easy shortcut for generating empty content. And we do all this through a proprietary method: blue4AI, a framework that ensures agile deliveries, with clear steps and consistent objectives, aligned with each client's strategy. This method is not a theoretical concept: it was created from our practical experience and today is one of the pillars that guarantees the quality and predictability of our projects. Before the hype, look at the ROI. Workslop is not just a conceptual fad. It's a warning that rushing to adopt AI without governance, without well-structured data, and without clear objectives can be costly. Just as we saw in the past with cloud computing, many today view AI with suspicion. But we know that, when applied correctly, it transforms businesses and opens new avenues for growth. The difference between hype and results lies in how the technology is implemented. Our experience shows that it's entirely possible to capture real value with AI, provided the project is thought out from start to finish: from data to strategy, from execution to ROI. At BlueMetrics, we follow a simple principle: AI isn't about doing less work, it's about generating more value. Gabriel Casara is CGO at BlueMetrics and believes in the value of intelligence, whether artificial or not. Want to see GenAI, Machine Learning, and data solutions making a difference in your company?

Or: How to use AI to help your customers choose and convert more Denis Pesa, BlueMetrics CEO AI-generated image AI-generated summary: In this article, Denis Pesa, CEO of BlueMetrics, shares a personal experience when trying to buy a jigsaw and how he ended up using artificial intelligence to make his decision with more clarity and less frustration. Using this practical example, Denis presents two high-impact AI applications for e-commerce — virtual sales assistants and automatic product file enrichment — and shows how BlueMetrics has implemented these solutions with agility, affordable cost, and robust technological base. The jigsaw drama As an engineer by training, I confess: I'm a pain when it comes to buying things. I compare everything, read specifications, watch videos on YouTube and end up suffering physically when deciding which item to buy. There are so many options. It's torture. Yesterday, for example, I decided to organize the garage storage area by installing some shelves. To do this, I needed a jigsaw. My level of manual skills is ridiculously low, but let's leave that aside. The problem is: when I looked for the saw on the usual e-commerce sites, the torment began. Did you know that there are dozens (maybe hundreds) of jigsaw models? Which one is good? Which one is right for me? The most expensive one, full of features, or the cheap basic one? I selected three options and clicked on “compare”. A table appeared with power, cutting angle, cable length… but nothing that really helped me decide. Annoying. AI to the rescue Normally, my next step would be: searching for reviews on Google, unboxing videos on YouTube, carpentry forums… But this time I decided to resort to something more modern (and sensible): I used AI. Prompt I sent to my favorite LLM: “I need to saw some wood to make shelves. I am not a professional and probably won’t use this saw very often after this. Show me the best and most suitable models on the market and suggest the best one for my needs. Consider low cost, quality and user reviews.” Hallelujah. Within seconds, I received: A list of four ideal models Personalized suggestion for my case Links to where to buy And the best part: without leaving the sofa, without opening 15 tabs, without worrying about it. What does this have to do with your e-commerce? Now, my dear e-commerce executive or manager, think about your customers. How many of them are experiencing exactly what I experienced? Lost among dozens of products, looking for answers outside of your website, and abandoning their carts along the way? Here are two AI applications that could have solved my problem — and improved your conversions: 1. AI-powered virtual sales assistant (And please don't call it a chatbot) An intelligent AI assistant, installed directly on your e-commerce site, that talks to the customer like an experienced salesperson: it understands the intended use, filters the most suitable products, explains the differences, and suggests the best option. Instead of leaving your site to ask my LLM, I would have sorted it out right there. And bought from you. 2. Automatic enrichment of product sheets (Product Augmentation) You register a product with brand, name, and code. The AI searches for external information, generates a complete description, usage instructions, differences and even comparisons with other items in your catalog, updating your product registration. All this without manual intervention. Imagine your customer reading an AI-generated description that answers exactly the question they didn’t even know how to ask. Other ideas you can explore with AI in your e-commerce: Personalized recommendations based on user behavior Dynamic pricing is automatically adjusted based on demand and competition Review rating and summary with sentiment analysis Smart support on WhatsApp or social media Automatic creation of ads and descriptions, SEO optimized Want to see   GenAI and Machine Learning   solutions   making a difference in your company? AI-generated image Want to put this into practice? A little over a year ago, all of this seemed like something out of a science fiction movie or expensive technology from big tech. But the tables have turned. Today, here at BlueMetrics , we are able to develop and implement this type of solution with AWS managed services, in just a few weeks, at an affordable cost, and in some cases, subsidized . But be careful: the market won't wait. Whoever moves first wins. Whoever delays becomes a benchmark case... for others. Want to know how to apply this to your business? Send me a message here and we can schedule a chat. What about the shelves? Well, we’ll find out soon enough, but as a friend of mine says: “50% chance of success. 80% chance of failure.” Denis Pesa is the CEO of BlueMetrics and a passionate DIY enthusiast.

Personalization in candidate guidance Automation of course recommendations Scalability and efficiency with artificial intelligence AI-generated image AI-generated summary: One of the largest educational institutions in Brazil has implemented a virtual assistant based on generative AI to improve student recruitment and offer personalized guidance on choosing courses and teaching methods. The solution has brought significant gains in agility, efficiency, and quality of service, enabling 24-hour support, automated information collection, and intelligent lead qualification, transforming the journey of future students and consolidating the institution as a reference in educational innovation. Overview The client in question is one of the largest educational organizations in Brazil, with more than 400,000 students in several higher education institutions and hundreds of educational centers. This client's goal was to improve the experience of its future students through technological innovation. With the advancement of technology and the growing need for digital career guidance, it became essential to create an automated, agile service capable of personalizing the student recruitment journey. In addition, the institution needed efficient integration between its channels and CRM tools to record and analyze each interaction. It was in this context that BlueMetrics developed an innovative generative AI solution, capable of taking the process of guiding and recruiting new students to another level. Market context: Growing need for digital career guidance for future students; Complexity in choosing between different courses and teaching modalities; Demand for technological solutions that optimize recruitment and relationships with potential students; Importance of integration between communication channels and CRM in the educational sector; Need for personalization and agility in the first contact with the future student. Problem: How to improve the journey of choosing and recruiting new students? As a prominent player in a highly competitive market, the client faced significant challenges, such as scaling its service capacity while maintaining and even expanding the level of perceived service quality. According to Gabriel Casara, CGO of BlueMetrics, "These are very suitable use cases for artificial intelligence. With a well-designed project, it is possible to do not only more, but also better, with fewer resources." Main challenges: Operational limitations: Dependence on human teams to respond to recurring queries; Low simultaneous response capacity, creating bottlenecks in periods of high demand; High consumption of time and resources in initial support for potential students. Business limitations: Lack of an intelligent and automated guidance channel; Difficulty in offering personalized and agile service; Lack of integration between interactions and monitoring systems. Technological limitations: Lack of structured and intelligent capture of information from interested parties; Lack of automated analysis of frequently asked questions; Limitation on the scalability of digital services. The solution: Generative AI for personalized guidance and efficient recruitment AI-generated image Imagine that you are a student looking for information about the different courses and options available at a university network. In just a few seconds, you access a virtual assistant that speaks naturally, understands your questions, presents the best alternatives, and even collects your information to facilitate future contact. This dynamic and intelligent flow offers personalized and enlightening guidance, helping the student to make decisions safely and quickly. The institution has therefore implemented an intelligent virtual assistant, with infrastructure 100% based on AWS and Generative AI, designed to offer assertive guidance to future students. According to Diórgenes Eugênio, Head of Gen AI at BlueMetrics, “This project was undoubtedly the most comprehensive development we have ever done regarding virtual assistants. In addition to the conversation flow with queries in knowledge bases, we implemented other features, such as models for capturing information during conversations, models for summarizing the assistant’s conversation with the lead, and also an integration that collected all the summaries of conversations from the last 24 hours daily and automatically integrated them with Salesforce. This project went beyond just delivering an interface for the sales funnel, delivering an end-to-end integration of the relationship with leads.” Main Components: Virtual assistant with advanced conversational communication; Constantly updated institutional database ; Web scraping for automatic collection of information about courses; Automatic student data extraction by generative AI; Storing interactions and generating summaries for the capture team; Native integration with Salesforce. Technological Differentials: 100% cloud architecture on AWS; Using Amazon Bedrock in different layers of generative AI; Model training with exclusive knowledge of the institution; Continuous evolution of the knowledge base; Automation in capturing and qualifying leads. Immediate Benefits: 24-hour service , with precise and personalized guidance; Reduction of the operational burden on human teams; Better experience in the decision and pre-enrollment phase; Automatic collection of contact information; Generation and qualification of leads in a structured way. Want to see   GenAI and Machine Learning   solutions   making a difference in your company? Results: The implementation of the virtual assistant brought substantial changes to the institution's recruitment process, increasing the efficiency of service and making the decision-making process more fluid and informative. Impacts on collection: A tool that guides potential students in choosing courses and modalities; Reducing complexity in decision-making for entry into higher education; Agile and structured service at key moments of the journey. Enhanced Candidate Experience: Continuous support, available at any time; Clear and accurate information about courses and institutions; Minimization of doubts and communication barriers. Technological advancement and integration: Intelligent lead qualification using generative AI; Automated processing of candidate information; Support for strategic decision-making with data on behaviors and doubts. This initiative positions the educational institution as a reference in innovation in attracting new students, using cutting-edge technology to create a personalized journey from the first contact. Technologies used The solution was designed using several AWS technologies, including: AWS Services Bedrock OpenSearch S3 StepFunctions DynamoDB Lambda API Gateway Cognito EC2 Languages, Libs, and Frameworks Python Conclusion: The creation of the virtual assistant redefined the institution’s relationship with its new students from the very first contact opportunities, through a significant increase in the quality of the customer experience. “We believe in the power of technology to bring people and important choices closer together. This project materializes this in practice,” comments Gabriel Casara, CGO of BlueMetrics. How about creating a case like this for your company? Let's schedule a call? Discover some Use Cases . About BlueMetrics BlueMetrics was founded in 2016 and has already delivered more than 160 successful solutions in the areas of Data & Analytics, GenAI and Machine Learning for more than 70 companies in the United States, Brazil, Argentina, Colombia and Mexico. It has its own methodology and a multidisciplinary team focused on delivering solutions to real challenges in the business world.

Imagem gerada por IA AI-generated summary: Artificial intelligence is redefining efficiency in companies by transforming data into decisions and automating previously manual processes. Technologies such as Machine Learning and GenAI allow for predicting demand, optimizing inventory, reducing errors, and increasing the productivity of administrative teams. Real-world cases in various sectors prove that AI generates significant savings and makes operations more precise. One example is BlueMetrics' project for one of Brazil's largest TV networks, which automated the transcription and summarization of news reports using generative AI, reducing costs and ensuring impartiality. With over 200 AI and data projects delivered to more than 90 clients in Brazil, the US, and Latin America, BlueMetrics demonstrates that the future of business efficiency is driven by data and applied artificial intelligence. Imagine being a manager tasked with reducing costs amidst an uncertain economic landscape. Spreadsheets have been meticulously analyzed, contracts renegotiated, and margins squeezed to the limit. Yet, the pressure for efficiency persists. This is where artificial intelligence (AI) becomes more than just a technological tool: it becomes a strategic ally. In recent years, companies of all sizes and sectors have discovered that data, Machine Learning (ML), and generative AI (GenAI) solutions can do much more than automate tasks: they transform how resources are used and how decisions are made. The result is a leaner, more predictable, and smarter operation. From operational efficiency to business intelligence. Reducing costs has always been a business goal. But with the advancement of AI, this goal has come to be achieved not only by cutting expenses, but also by increasing the efficiency of every available resource, whether human, financial, or material. AI learns from historical data, identifies patterns, and proposes ways to improve even before bottlenecks become problems. According to the consulting firm McKinsey, companies that adopt AI at scale achieve average gains of up to 20% in operational efficiency and cost reductions ranging from 10% to 15%, depending on the sector. This data-driven intelligence changes the traditional logic of cost management. The focus shifts from "how much we spend" to "how we spend" and "what we could predict before the expense occurs." Intelligent automation and gains in the back office. In administrative areas, AI is replacing manual and time-consuming processes with automated and integrated workflows. Copilots in finance, human resources, and support, for example, can interpret and process data from documents, invoices, or expense reports in seconds, significantly reducing execution time and the likelihood of human error. These intelligent assistants also help identify inconsistencies and cost-saving opportunities. An AI system can, for example, analyze contracts and detect unfavorable readjustment clauses, suggesting automatic renegotiations. The result is a more agile back office, with teams focused on strategic activities instead of repetitive tasks. Supply chain and customized inventory In sectors such as retail, industry, and logistics, AI has become essential for adjusting inventory and reducing losses. Machine learning models analyze variables such as seasonality, purchasing behavior, weather conditions, and even macroeconomic data to accurately predict demand. As a result, companies stop relying on manual estimates and start operating with customized inventories, reducing storage costs and avoiding stockouts. In addition to direct savings, AI brings predictability, which, in itself, is a highly valuable asset in increasingly dynamic markets. Predictive maintenance and reduction of unexpected downtime. Another field where AI generates measurable impact is asset maintenance. In factories, transportation companies, or energy utilities, sensors connected to predictive models make it possible to detect failures before they happen. These systems analyze vibration, temperature, energy consumption, and other signs of wear in real time, anticipating the need for repairs and preventing unscheduled downtime. In addition to reducing corrective maintenance costs, this approach maximizes equipment utilization and extends its lifespan, directly impacting the bottom line. From data to decision: the strategic role of AI. More than just an automation tool, AI is a decision support instrument. It transforms scattered data, such as sales, inventory, productivity, weather, traffic, or customer behavior, into actionable information. This allows managers to make decisions based on evidence, not just intuition. This shift in mindset is what differentiates companies that merely "use technology" from those that operate with data intelligence. Cost reduction ceases to be a one-off measure and becomes a continuous optimization process, supported by learning and constant improvement. Next, we will look at some real-world examples of cost reduction and resource optimization. Imagem gerada por IA Real-world success stories with AI and data. 1. Festo: Predictive maintenance and cost savings per machine Festo, an industrial manufacturer, has implemented an AI solution for predictive maintenance in machine tools. The system monitors real-time data such as vibration, temperature, and dynamic behavior, and alerts users to anomalies before they become failures. This has resulted in estimated savings of US$16,000 per machine in avoided costs and rework. This case illustrates how, even in highly technical operations, the application of anomaly models and forecasting algorithms generates a quick return, with payback often in less than a year. 2. Novelis: From corrective to predictive maintenance Novelis, a global leader in aluminum production, transitioned from a reactive approach to a predictive strategy based on AI. Using sensors and analytical platforms, they began predicting wear and tear and failures in their assets before interruptions occurred. This allowed them to reduce unexpected downtime, increase equipment availability, and save on corrective maintenance. For companies that deal with expensive assets and continuous use, this type of change in operational culture can generate a direct and repeatable impact. 3. ENGIE Digital: predictive maintenance in energy infrastructure ENGIE Digital used AWS SageMaker to develop predictive maintenance use cases for its equipment (power plants, compressors, etc.). This enabled them to model the asset lifecycle, detect anomalies, and anticipate parts replacements. For an energy company, reducing failures or optimizing maintenance means fewer forced shutdowns, control over energy consumption, and lower operating costs over time. 4. Bosch: Real-time monitoring and AI for maintenance Bosch has implemented IoT sensors connected to AI models to monitor parameters such as vibration, temperature, and pressure in its equipment. This allows it to detect wear patterns and impending failure before a device becomes a bottleneck. This type of data-driven automation allows the maintenance team to focus their efforts precisely on critical cases, reducing redundant inspections and premature replacements. 5. Penske: Fleet maintenance with AI Penske, a truck rental and fleet management provider, adopted a platform called Fleet Insight that integrates telematics (onboard sensors) and an AI model that monitors hundreds of millions of data points per day. This solution anticipates mechanical failures and allows for scheduling interventions before they increase fleet downtime costs. One of Penske's customers, Darigold, uses these insights to predict component replacements such as tires or hoses, comparing the cost of downtime versus the cost of preventative maintenance. 6. Mount Sinai Hospital: AI for hospital management In the healthcare sector, Mount Sinai Hospital in New York uses AI to predict which patients are at high risk of hospitalization based on medical histories and vital signs. This allows for optimized allocation of beds and hospital resources, reducing costs associated with underutilized occupancy and unforeseen events. The hospital claims to have achieved a reduction of approximately 20% in costs associated with bed management. This type of application demonstrates that, even in sensitive and regulated environments, AI can act as a strategic support for operational efficiency. 7. Konux + Deutsche Bahn: predictive railway maintenance The German startup Konux has developed an AI + IoT solution to monitor critical components of the railway network, especially the so-called "points" (rail switches). Deutsche Bahn hired Konux to monitor hundreds of switches, later scaling to thousands of assets. The system generates wear and failure predictions, allowing maintenance to be scheduled without compromising rail operations. This case clearly demonstrates how AI can be applied to heavy infrastructure, with high criticality and a need for high reliability. Want to see   GenAI and Machine Learning   solutions   making a difference in your company? BlueMetrics Case Study: How one of Brazil's largest TV networks automated the transcription of its content with GenAI. Context One of Brazil's largest television networks, with a national presence and a strong presence in investigative journalism and crime reporting, sought to increase its operational efficiency and accelerate the multiplatform distribution of content. In an increasingly competitive and digital market, the broadcaster faced the challenge of rapidly transforming its vast audiovisual archive into standardized, accessible, and impartial textual information. The growing demand for structured digital content and the accelerated pace of newsrooms have made evident the need for a technological solution capable of automating tasks that were previously manual, while maintaining the rigor and neutrality required by professional journalism. Problem The process of transcribing and summarizing news reports was entirely manual, requiring time and dedication from specialized professionals. This workflow generated high operational costs, delays in making the reports available in different formats, and inconsistencies in the summaries produced by different editors. The absence of a structured textual database also prevented the full utilization of the journalistic archive, limiting the reuse of materials and hindering integration with other digital platforms. The challenge was to find a solution capable of automating the processing of large volumes of audiovisual content, while maintaining the accuracy, impartiality, and agility necessary for the newsroom environment. Solution BlueMetrics has developed an automated transcription and summarization solution based on Generative AI and AWS cloud services, combining AWS Transcribe for audio-to-text conversion and AWS Bedrock for generating unbiased summaries. The project included the creation of a complete processing pipeline, integrating components such as: Automated audio-to-text transcription system; Summary generation engine with neutrality control and fact-checking; Structured database for storage and querying; Scalable serverless architecture with native integration into the client's existing infrastructure. According to Diórgenes Eugênio, Head of GenAI at BlueMetrics, “the biggest challenge was ensuring that the summaries did not express any kind of bias or opinion. The combination of Transcribe, Bedrock, and our customized validation layer was essential to delivering a pipeline aligned with the broadcaster's editorial standards.” This approach allowed not only the automation of processes, but also the incorporation of linguistic validations specific to crime journalism, ensuring terminological accuracy and editorial consistency. Results The solution transformed the journalism team's workflow. Transcription and summarization time was reduced from hours to minutes, freeing up journalists and editors for higher-value activities such as investigation and story curation. The broadcaster began making its content available in a more agile and standardized way across multiple digital channels, increasing its coverage capacity and the ability to reuse its historical archive. In addition to operational efficiency, the project brought significant editorial gains, with consistent, neutral summaries that comply with the impartiality standards required by investigative journalism. Among the main results achieved are: Complete automation of the transcription and summarization process; Significant reduction in content processing time; Standardization and neutrality in the generated texts; Organization and structuring of the journalistic archive; Better use of content across multiple platforms. The adoption of Generative AI not only optimized costs, but also raised the standard of quality and productivity in the processing of audiovisual content, positioning the broadcaster as a benchmark for innovation within the Brazilian television sector. Conclusion Intelligent automation and the strategic use of generative AI are redefining how companies manage their processes and resources. In the case of the broadcaster, BlueMetrics demonstrated how solid data engineering, combined with GenAI applied with technical and ethical rigor, can transform an operational challenge into a competitive advantage. This expertise is what sets BlueMetrics apart in the market. The company combines deep mastery of data engineering, analytics, and machine learning with a practical, results-oriented approach, ensuring that every solution delivered generates measurable value. With over 200 AI and data projects completed for more than 90 clients in Brazil, the United States, and throughout Latin America, BlueMetrics continues to help organizations across various sectors reduce costs, optimize resources, and operate more intelligently and efficiently. In a world where efficiency is synonymous with competitiveness, we develop data and AI solutions that deliver measurable, short-term results. Does your company need to reduce costs and optimize resources? Let's talk about it. Learn about some Use Cases

Imagem gerada por IA AI-generated summary: The adoption of artificial intelligence has ceased to be just a promise and has become a competitive differentiator for companies seeking to increase productivity and operational efficiency. More than just automating repetitive tasks, AI, machine learning, and GenAI solutions allow for accelerated workflows, optimized strategic decisions, system integration, and scalable operations at lower costs. Case studies from companies like Renault, Mitsui, TVS Supply Chain Solutions, Samsung SDS, and ANZ Bank demonstrate real gains when the technology is applied in a customized way, using proprietary data and models adapted to the business context. In Brazil, BlueMetrics implemented a solution for a large manufacturer of heavy commercial vehicles, reducing production analysis time from 4 hours to just 6 seconds. These examples prove that AI solutions cannot be generic: they need to be custom-developed, with governance and security, to generate concrete and sustainable results. Productivity and operational efficiency are central themes for companies seeking growth in highly competitive environments. More than simply cutting costs, it's about finding intelligent ways to extract more value from time, resources, and available human capabilities. In this scenario, artificial intelligence (AI) presents itself as a catalyst for direct impact: applied strategically, it allows for the reduction of manual activities, the optimization of decisions, and the creation of more agile and connected workflows. The key difference lies in the combination of high-quality data, machine learning models, and the latest generative AI (GenAI) solutions. Together, these technologies not only accelerate existing tasks but also open up opportunities for new ways to organize operations, integrate systems, and support professionals at all levels of the organization. How AI, data, and machine learning drive productivity and efficiency. The application of AI to productivity can be observed at different levels of business operations. Below, we explore the most relevant areas for medium and large-sized organizations: 1. Intelligent process automation While traditional automation has already proven efficient in repetitive and structured workflows, AI expands this potential by handling unstructured data, text, images, and even human interactions. This allows activities that previously required hours of manual work to be transformed into operations executed in minutes, with a smaller margin of error. Examples include automated email sorting, contract analysis, call center classification, or data extraction from tax documents. These gains are not limited to time saved, but also free up professionals for more strategically valuable roles, such as innovation and customer relationship management. 2. Accelerating workflows with copilots and AI agents. Copilot tools and AI-based agents act as cognitive assistants that accompany professionals throughout their routines. They suggest answers, anticipate steps, and integrate information from multiple sources, preventing rework and increasing execution speed. In practice, an analyst can create reports with the support of AI that already provides consolidated data and ready-made visualizations; a manager can plan projects with automatic recommendations on deadlines and priorities; and a sales team can rely on predictive insights into which customers are most likely to buy. This type of acceleration transforms how workflows occur, making work more agile and proactive. 3. Support for decision-making and prioritization AI is especially valuable when used to analyze large volumes of data and extract patterns that guide strategic decisions. Predictive models can indicate market trends, forecast demand, or identify operational risks, allowing managers to more clearly prioritize where to allocate efforts and investments. Furthermore, AI assistants can support daily executive tasks by organizing schedules, identifying bottlenecks in timelines, and suggesting resource redistributions. This layer of support helps ensure that time is used more efficiently and focused on the results that truly matter. 4. Systems integration and orchestration In many companies, technological fragmentation remains an obstacle to productivity. AI can act as an integration layer, connecting systems that historically haven't "talked" to each other. Through natural language processing and machine learning techniques, it's possible to reconcile information from ERPs, CRMs, marketing tools, and BI platforms, creating a single, coherent view of operations. This intelligent integration reduces redundancies, minimizes manual data entry errors, and allows teams to access information more fluidly and reliably. In practice, the company functions as an interconnected organism, without the friction that usually characterizes complex operations. 5. Continuous innovation and economies of scale. In addition to immediate gains, the adoption of AI also paves the way for innovation and scalability. Processes that previously depended on a growing number of people can be scaled up through algorithms that learn from data. This means that the company can grow in volume of operations without necessarily growing proportionally in costs or teams. This gain in scale becomes even more significant when combined with GenAI, which can quickly generate content, simulations, or prototypes, accelerating innovation cycles and reducing the time between idea and execution. Real-world examples of AI driving productivity and efficiency. Renault: Digital twins and AI for energy efficiency in manufacturing. The French automaker Renault has implemented a system of digital twins and AI algorithms at its plant in Palencia, Spain, capable of processing billions of data points per day from cameras, sensors, and 3D scanners. The goal was to optimize quality inspections, reduce waste, and better control energy use. Results: Since 2021, the project has enabled a 26% reduction in energy consumption per vehicle produced. In addition, it has improved the accuracy of fault detection and increased maintenance and logistics efficiency. Source: Cadena SER Mitsui & Co.: Accelerating document review with GenAI Mitsui, a Japanese conglomerate with a global presence in commerce and projects, faced lengthy document review cycles in international tenders and contracts. To address this challenge, it developed, using the AWS ecosystem, a GenAI-based solution applied to corporate documents, leveraging language models tailored for legal and contractual data. Results: a 40% to 80% reduction in review time, lower risk of human error, and freeing up specialists for strategic activities such as negotiation and proposal customization. TVS Supply Chain Solutions: internal assistant with customized LLMs The logistics company TVS Supply Chain Solutions developed a "Sidekick," an internal AI assistant based on LLMs trained and tuned with the company's own data. The goal was to support employees with internal queries, operational reports, and systems integration. Highlights: The project not only delivered efficiency gains in day-to-day operations, but also provided important lessons on governance, data security, and organizational acceptance. The experience showed that it is possible to integrate GenAI into mission-critical processes in a controlled manner. Samsung SDS: Intelligent automation at enterprise scale. Samsung's technology subsidiary has internally developed the Brity RPA platform, which combines automation bots with AI to interpret logs, recommend processes, and perform administrative tasks in areas such as IT, procurement, and auditing. Results: In just nine months, the solution was adopted by approximately 15,000 employees, generating an estimated savings of 550,000 work hours. The approach demonstrated that when AI is incorporated into corporate infrastructure, it can free up a massive amount of time and resources. Source: Wikipedia — Samsung SDS ANZ Bank: Copilots integrated into software development The Australian bank ANZ conducted an internal pilot with GitHub Copilot integrated into its software engineering workflows. The project involved approximately 1,000 developers and sought to measure the impact on productivity and code quality. Results: Teams reported gains in code production speed and higher quality in repetitive programming tasks. The study also revealed governance and standardization challenges, but demonstrated how copilots can generate gains when adapted to the corporate context. Source: ArXiv These cases show that adopting AI for productivity goes beyond using generic tools. They involve solutions developed or customized for the context and data of each company, with a real impact on operational efficiency, cost reduction, and economies of scale. Renault, Mitsui, TVS, Samsung SDS, and ANZ Bank are examples of organizations that have successfully transformed critical workflows with AI, demonstrating that the technology, when applied in a targeted way, delivers concrete and sustainable benefits. Want to see   GenAI and Machine Learning   solutions   making a difference in your company? A BlueMetrics case study: linear programming to accelerate analysis and optimize production in industry. Context One of the largest truck and bus manufacturers in Latin America, operating throughout the region and with a complete portfolio of heavy vehicles and passenger transport vehicles, was seeking new ways to increase the efficiency of its assembly line. In a sector marked by high operational complexity, deadline pressures, and increasingly tight margins, the company identified that production planning was a critical point for maintaining competitiveness. Problem The production feasibility analysis process was done manually, consuming approximately 4 hours per day. This represented about 80 hours of repetitive work per month, subject to human error. In addition to wasted time, planning failures could lead to non-optimized sequencing, line stoppages, and delivery delays, directly impacting productivity and inventory costs. The challenge was clear: to find a solution that could automate data collection, make the decision-making process more reliable and agile, and at the same time provide accessible information to operations teams. Solution BlueMetrics implemented a linear programming-based optimization platform, custom-developed to meet the client's needs. The project involved building a robust data pipeline that automatically extracts information from spreadsheets and internal systems, transforming it into optimized structures for analysis. From there, the linear programming algorithm calculates the best production sequence in seconds, considering inventory constraints, component availability, and production targets. The solution also includes an intuitive dashboard that presents the results clearly and accessibly, hosted in a cloud environment with scalability and security. As a result, the analysis went from being manual and slow to being automated, reliable, and virtually instantaneous. Results Operational efficiency: reducing analysis time from 4 hours to just 6 seconds, eliminating 99.96% of the effort previously required. Productivity: Elimination of approximately 80 hours of manual work per month, allowing teams to direct their time and energy towards higher-value activities. Production optimization: more efficient sequencing, maximizing the number of vehicles produced per period, and better utilization of available resources. Financial impact: reduced inventory costs and increased revenue potential through optimized production capacity. Qualitative benefits: increased predictability, greater agility in decision-making, and scalability of the process to other production scenarios. This case demonstrates how applying optimization algorithms combined with sound data engineering can profoundly transform productivity in industrial environments. By drastically reducing analysis time and making the process more accurate and scalable, BlueMetrics has reinforced its role as a strategic partner in the practical application of AI and process optimization for industry. Conclusion The examples presented demonstrate that artificial intelligence can, in fact, generate significant gains in productivity and operational efficiency when applied in a structured way. But they also make one essential point clear: these gains do not happen automatically. Conversely, when generic solutions are applied without considering the specific reality of each organization, there is a risk of wasted resources, low adoption by teams, and even loss of efficiency. For the results to be real and sustainable, it is essential that AI solutions are customized to the specific business challenges of each company. This involves three pillars: Use of proprietary, well-structured data capable of supporting accurate and contextualized analyses. Training and adapting LLMs to the specific domain, ensuring relevant responses and adherence to critical processes. Robust layers of security and governance that ensure reliability, information protection, and regulatory compliance. In other words, AI cannot be treated as a "one size fits all" technology. Each company has unique characteristics, legacy systems, strategic goals, and constraints that need to be incorporated into the solution design. This is precisely where BlueMetrics differentiates itself. With over 200 successfully delivered projects for more than 90 clients in the US, Brazil, and Latin America, the company combines expertise in data, machine learning, and GenAI to develop high-impact solutions that are fully aligned with each client's business. This experience allows them not only to implement AI but to make it truly productive, scalable, and strategic. Therefore, the path to increasing productivity and efficiency with AI lies less in adopting generic tools and more in building tailor-made solutions, supported by solid data, contextualized models, and mature governance. It is this alignment that ensures the technology becomes a competitive advantage, and not just another layer of complexity. Shall we discuss this? Learn about some Use Cases

Imagem gerada por IA AI-generated summary: The use of AI and data is transforming agribusiness at every stage of the chain, from crop planning to marketing. Technologies such as Machine Learning and GenAI allow for more accurate demand forecasting, real-time crop monitoring, reduced input costs, increased productivity, and meeting growing sustainability demands. Real-world cases in the United States and Latin America demonstrate concrete gains in operational efficiency and competitiveness. With over 200 AI and data projects delivered to more than 90 clients, BlueMetrics combines data expertise with a robust portfolio of proven solutions, ensuring agile implementations and measurable short-term results for producers and managers in the sector. Agribusiness has always been a strategic sector for the Brazilian economy, responsible for a large part of the GDP and global leadership in several production chains. But in recent years, the pace of change has accelerated. Climate change, pressure for sustainability, a shortage of skilled labor, rising input costs, and the need to integrate marketing channels have created a scenario in which "doing things the way they've always been done" is no longer sufficient. It is in this context that AI and data emerge as crucial allies for increasing efficiency, reducing risks, and improving competitiveness. More than just dashboards and static reports, modern solutions combine Machine Learning and GenAI to anticipate scenarios, simulate impacts, and support real-time decision-making. From field to market: where algorithms make the difference. Managers of medium and large agribusiness ventures are dealing with increasing complexity. Producing more and better remains the goal, but now it needs to be reconciled with tight margins, regulatory requirements, and new customer and consumer expectations. In this environment, the use of AI and data ceases to be a trend and becomes a competitive differentiator. A clear example is in crop forecasting. Machine Learning models can cross-reference historical production data with external variables, such as climate, soil quality, rainfall levels, and even futures market information, to project productivity with much greater accuracy. This allows for planning input purchases, hiring, and even marketing strategies before the harvest even begins. Another fertile field for application is the management of climate and phytosanitary risks. AI algorithms are able to detect patterns that indicate the risk of pests or diseases before they spread, using satellite imagery, drones, and field sensors. Climate forecasting models, when combined with historical analyses, help define the best planting or harvesting window, reducing losses. Productivity and sustainability as two sides of the same coin. One of the great challenges of modern agribusiness is producing more with less environmental impact. Here, the application of AI and data generates direct gains. Intelligent systems automatically adjust irrigation based on soil moisture and weather forecasts, avoiding water waste. Fertilizers and pesticides can be applied in a localized way, only where they are needed, reducing costs and increasing the sustainability of the operation. This movement aligns with the growing pressure from investors, international buyers, and regulatory bodies, who demand more transparent and sustainable supply chains. The use of GenAI can even support the generation of compliance reports and the consolidation of environmental, social, and governance (ESG) indicators, simplifying a process that was previously expensive and bureaucratic. From planning to marketing While the gains are evident within the farm gate, opportunities exist outside it as well. The consumer market is increasingly digital and demanding. Platforms supported by GenAI help producers and cooperatives personalize offers for different channels, generate pricing insights, and simulate marketing scenarios. For large agribusiness groups involved in exporting, the use of AI also allows them to cross-reference variables such as exchange rates, international demand, and logistics, offering an integrated view to define the best sales strategy. More than just increasing efficiency, the integration of AI and data in agribusiness represents the possibility of transforming management: less dependent on intuition and more guided by evidence, anticipation, and intelligence. Imagem gerada por IA From concept to practice: where AI and data are already transforming agribusiness. In the first part, we conceptually explored how AI and data, in the form of technologies like Machine Learning and GenAI, can support agribusiness in various ways. Now, it's time to look at concrete experiences. Several companies and institutions are already applying these technologies in their daily work in the field, achieving productivity gains, cost reductions, and greater predictability. These examples serve as inspiration for managers of medium and large enterprises who wish to accelerate their digital journey without relying solely on intuition or retrospective analysis. 1. Case Study: FarmWise Labs (United States): Weed control using computer vision and robotics. Problem In vegetable gardens and diversified plantations, weed management would require the extensive use of herbicides or manual intervention, both of which are expensive, environmentally impactful, and imprecise. In many cases, herbicides are applied indiscriminately, even where there is no infestation, leading to chemical waste and unnecessary costs. Solution The company FarmWise Labs has developed automated mechanical weeding robots that use AI, computer vision, and robotics to identify and remove weeds in vegetable crops. The Titan FT-35 robot, for example, visually analyzes the soil and crops to distinguish weeds from cultivated plants, removing unwanted soil without affecting the main crops. They offer this service charging per acre treated, which allows the farmer to outsource weeding using state-of-the-art technology, without needing to purchase equipment or have a high level of operational expertise. Observed results: Reduced use of chemical herbicides, as intervention becomes localized instead of widespread application. Lower operational costs related to manual weeding or maintenance of weeds after excessive chemical application. Lower environmental impact, with less soil and water contamination, in addition to contributing to more sustainable agricultural practices. Although there are no publications quantifying all the gains in broad percentages in the public domain, the business model has already been recognized as an innovation in efficiency and sustainability. 2. Case IH (LatAm): precision agriculture, connectivity and self-management Problem In large-scale agricultural operations, machines, tractors, harvesters, and sprayers operate under variable conditions: terrain, soil type, weather conditions, and performance vary according to use. Without reliable real-time data or intelligent automation, operational efficiency falls short of ideal, in addition to costs associated with maintenance, fuel consumption, and human error. Solution Case IH has unveiled a digital ecosystem for precision agriculture that incorporates connectivity, digitization, and machine learning to enable managers to remotely monitor and optimize operations, and allow machines to self-adapt. Some details: Machines equipped with multiple sensors (temperature, humidity, operating conditions) that allow real-time monitoring. Systems that automatically adjust operating modes (e.g., harvesting), self-regulating machine parameters based on terrain/sensor data to maximize yield or reduce wear. Observed results Increased operational productivity on machines, with less rework and less unproductive time. Fuel economy, less machine wear and tear (more effective preventive maintenance). Improved resource efficiency, including reduced environmental impact. 3. AgroTIC Case Study (Colombia): Smartphone + ML use for producers Problem Smaller or medium-sized producers in areas like Santander (Colombia) have limited access to agronomists and technical advice, little real-time visibility into crop health, and difficulties connecting to markets efficiently. Solution A smartphone-based application that allows for crop health monitoring with the assistance of agronomists. It uses deep learning/ML techniques to process images captured by the producer, identifying diseases or deficiencies in plants. A platform that connects producers with buyers/markets, helping with the sales process. Observed results Improved crop quality, as problems are identified earlier. Higher yields and reduced losses due to plant health problems. Better product valuation in the market, due to quality. 4. Case Omdena (United States): mapping of agroforestry systems via satellite + AI Problem Agroforestry systems are productive systems that combine trees with agricultural crops, benefiting biodiversity, sequestering carbon, and improving the soil. However, many of these systems are not accurately mapped, which hinders planning, monitoring, verification of carbon credits, and decision-making. Without mapping, there is low visibility into the exact location, extent, and characteristics of these systems, making it difficult to implement policies, incentives, and land-use management. Solution Omdena's "Mapping of Agroforestry Systems based on Artificial Intelligence" project uses satellite imagery with AI algorithms to automatically identify agroforestry areas, distinguish types of vegetation cover, and delineate these areas on georeferenced maps. This allows institutions, producers, and governments to visualize where agroforestry systems exist, their size, and their composition, with much greater granularity than traditional inventories. Observed results Improved spatial visibility of agroforestry systems, with detailed maps that allow for the identification of areas that can be restored or that are already operating within the system, with greater precision than manual surveys. These maps could be used for carbon credit programs, incentive policies, or sustainability monitoring. Accurate detection allows for faster forest or agroforestry planning, facilitates decision-making, and supports stakeholders (producers, regulators, buyers) with reliable data. Want to see   GenAI and Machine Learning   solutions   making a difference in your company? How can BlueMetrics apply these learnings? These cases clearly show that, for medium and large-scale agricultural operations, the intelligent combination of Machine Learning, AI, and data produces real gains: productivity, cost reduction, better use of inputs, operational efficiency, and sustainability. At BlueMetrics, with over 200 AI and data projects for more than 90 clients in the United States, Brazil, and Latin America, we have a vast repertoire to adapt these solutions to your business. Here are some areas of our expertise that guarantee accuracy and speed in achieving results: Data quality : We know that any AI model depends on clean, well-structured, and integrated data. We have experience in diagnosing, cleaning, enriching, and integrating diverse databases (satellite, sensors, ERP, logistics). Choosing the right technology : not every project needs GenAI; but when we combine GenAI with Machine Learning and automation, we create more robust solutions. For example, recommendation systems for input, or assistants that help managers simulate scenarios. Well-defined and scalable pilot projects : we typically start with a field or crop, or a key operation (e.g., pest monitoring, crop forecasting, logistics routing), show results in the short term, and then scale up. Concrete and measurable deliverables in the short term : because customers expect a return. With real benchmarks like the ones above, with established deadlines and goals (for example, a reduction in pesticide use of X%, an improvement in yield of Y%, or operational cost savings of Z%), it is possible to demonstrate value quickly. Conclusion: It's time to transform data into results in the field. Brazilian agribusiness has already proven to be a global economic engine, but the future demands a new layer of intelligence. AI and data are now key to meeting the challenges of productivity, sustainability, and competitiveness, transforming the sector into something even more efficient and resilient. For managers of medium and large enterprises, the time to act is now. The solutions already exist, case studies prove their effectiveness, and the competitive edge will come from those who can integrate technology, data, and strategy in a practical and fast way. At BlueMetrics, we believe in AI and data solutions for the real world: projects that don't stay in the lab, but deliver value in the field, in operations, and in the bottom line. The next step could be yours. Let's talk about it? Learn about some Use Cases .