Risc Technology, an NVIDIA Elite partner in Brazil, deployed virtual workstations with NVIDIA A40 GPUs for the architecture firm Königsberger Vannucchi, offering high performance, security, and a lower total cost of ownership than physical machines. Challenge: Virtualizing workstations for architecture The firm needed to give all architects access to high-performance machines, from any device, location, or network, without having to buy a physical workstation for each one. Th

Between September 29 and October 1, RISC Technology was a silver sponsor of the Electronic Warfare Symposium (SIGE) 2026 , held at the Technological Institute of Aeronautics (ITA) in São José dos Campos (SP). The event brought together professionals, researchers, institutions, and companies in technology, innovation, and defense to discuss current challenges and solutions that shape the future. For RISC, it was a chance to closely follow the demands for computing power, data

In the previous article, we explained why infrastructure is the true foundation of Artificial Intelligence. But understanding the theory is only the first step — the real challenge begins when a company needs to transform a traditional data center into an environment ready to train and run AI models. Unlike conventional workloads, AI requires structural changes that go far beyond simply purchasing more servers. Below is a practical guide to the essential steps. 01 Assess the

Artificial Intelligence has evolved from being merely a trend to becoming one of the primary drivers of digital transformation. Companies, universities, research institutions, and public organizations are investing in increasingly sophisticated models to automate processes, generate insights, and accelerate innovation. However, behind every AI-powered solution lies one essential element: the computing infrastructure. It is within this context that the concept of an AI Factory

With the rapid expansion of generative artificial intelligence, organizations are facing a critical challenge: how to reduce cost per token without compromising performance and scalability. In this scenario, choosing between Cloud or On-Premises AI Infrastructure has become one of the most important decisions for businesses seeking to balance costs, performance, and sustainable growth. Infrastructure decisions directly impact return on investment (ROI), data security, and the

The rise of generative AI has completely changed how companies develop, deploy, and manage artificial intelligence models. [1, 2] For years, MLOps was enough to structure traditional machine learning pipelines. However, the growth of Large Language Models (LLMs) brought new operational challenges that required a different approach: LLMOps. [1, 2] Today, understanding the difference between MLOps and LLMOps is essential for companies looking to scale generative AI with securit

GenAI and the New Challenge of Corporate Data Security Generative AI (GenAI) already plays a strategic role in data-driven organizations. Language models, corporate copilots, and intelligent agents are being integrated into critical business pipelines, driving productivity, automation, and innovation at scale. However, this advancement significantly expands the challenges related to information security and corporate data protection in GenAI environments. For senior data scie

For years, the evolution of artificial intelligence was dominated by a single focus: increasingly larger and more expensive models . More powerful LLMs, complex architectures, advanced fine-tuning, autonomous agents, and intelligent automation have come to occupy the center of technical decisions. But in 2026, an uncomfortable truth returned to the center of the debate: 👉 Generative AI doesn't fail because of a lack of intelligence. It fails because of bad data. With the ris

AI Agents have rapidly moved from academic experiments to the center of discussions about intelligent automation in enterprises. Frameworks like LangChain, AutoGen, CrewAI, and agentic approaches in large LLMs have popularized the idea of ​​systems capable of planning, deciding, and acting semi-autonomously. But for data, engineering, and architecture teams, the relevant question isn't whether agents are impressive, but rather: Do AI Agents represent a new leap in automation

Generative AI in production is no longer a laboratory curiosity. By 2026, virtually every data-driven company will have tested or is testing generative models for customer service, analytics, process automation, and decision support. The real challenge, however, is not proving that the technology works. Conducting a successful POC is relatively simple. The problem begins when generative AI leaves the experiment and starts operating as a critical business system. It is at this

NVIDIA GTC 2026 , one of the largest global events for artificial intelligence , accelerated computing , and high-performance infrastructure , once again brought together researchers, engineers, data scientists, and technology leaders. As is tradition, RISC Technology was present to closely follow the innovations that are shaping the future of computing and generative AI . Infrastructure for scalable AI: breaking new performance boundaries. Among the event's major hig

What Has Already Changed and What Will Still Change? Generative AI has evolved rapidly and become one of the most transformative technologies in the world of data science. What once seemed like mere laboratory experimentation now accelerates analysis, automates repetitive tasks, generates code, supports technical decisions, and allows for clearer communication with stakeholders. For professionals who balance technical depth, governance, deadlines, and scalability, generative

Why is multimodal AI the next big trend? Multimodal Artificial Intelligence is an evolution of traditional models, allowing systems to process different types of data text, image, audio, and video in an integrated way. This capability generates more precise and contextual responses, making the technology essential for complex applications. Imagine a medical diagnosis that combines imaging exams with clinical reports, or a security system that analyzes video and audio simultan

The year 2026 arrives with challenges and opportunities for companies that depend on technology. The combination of generative AI , scalable infrastructures , advanced security , and sustainability is shaping a new corporate landscape. In this article, you will discover the main IT trends that will impact the market in the coming months. 1. Responsible AI and Regulatory Compliance With the AI Act coming into effect in Europe and the LGPD (Brazilian General Data Protec

Digital transformation is demanding increasingly faster and more secure responses. In this scenario, Edge Computing emerges as a strategic solution for companies that need to process large volumes of data with low latency and high reliability. By bringing processing closer to the data source, this technology reduces dependence on the cloud, improves privacy, and ensures operational efficiency. But why is this approach so important for real-time Artificial Intelligence (AI)? A

The adoption of Artificial Intelligence (AI) in the cloud is growing exponentially, driven by generative AI solutions, intelligent automation, and scalable infrastructures. However, this evolution brings critical challenges: data security and regulatory compliance . With legislation such as the LGPD in Brazil and the AI Act in the European Union , companies need to ensure that their AI architectures are secure, ethical, and compliant. In this article, you will discover:

Demand for energy is growing rapidly, driven by digitalization, increased industrial consumption, and the expansion of technological infrastructures. At the same time, companies face pressure to reduce costs, meet ESG targets, and ensure regulatory compliance. In this scenario, Artificial Intelligence (AI) emerges as a strategic ally to transform energy management, making it more efficient, sustainable, and secure. This article explores how AI is revolutionizing energy manage

The rise of generative AI with models like GPT-4 , DALL·E , and Stable Diffusion is radically transforming how companies innovate , automate processes , and make strategic decisions . However, behind these revolutionary applications lies an essential element that underpins the entire ecosystem: scalable infrastructure . 🌐 Why is scalable infrastructure essential for generative AI? Generative AI models require: Massive volumes of unstructured data Robust computing ca

As the end of the year approaches, it's time to review technology investments and define strategic priorities. More than simply "using what's left over," this is the ideal time to optimize the IT budget, investing in infrastructure, information security, and technological innovation pillars that drive operational efficiency and business competitiveness. In a scenario where technology plays a leading role, the intelligent use of the budget can be the differentiating factor bet

The race for artificial intelligence isn't just happening in research labs or in the development of generative models; it starts in the data center. With the advancement of enterprise AI solutions, the world is witnessing an explosion in demand for computing power. Companies and governments are investing heavily in more powerful, sustainable data centers prepared to support large-scale AI workloads. But there is a central challenge: How can you grow without compromising ener