High-performance AI architecture meets deep research. Learn how distributed systems and private frameworks solve cost and security barriers.
1. Humberto, let’s begin with your journey—what inspired you to found MAGIC Research, and how did that shift shape your vision for AI-powered research?
We started working with AI in 2016, heavily focused on ML, data intelligence, and computer vision. Through my involvement at Concepta, FanHero, and other sister companies, we had opportunities to collaborate with major organizations, including Intel, Harvard, and SES AI, who were pushing the limits of AI and advanced computational intelligence. These collaborations spanned areas like drug discovery, molecular mapping, quantum physics simulations, and chip acceleration. The consistent challenge was cost: AI and advanced computation were prohibitively expensive, often slowing or even halting progress. We needed to maximize the computational resources we had — resources that weren’t always in the same geographic location. That’s when we began experimenting with building distributed and networked HPC systems to reduce the cost of advanced computation, particularly for research. As we advanced, it became clear this wasn’t just about one product or one company. Across all of our companies, AI work was happening separately, and each team was running into the same barriers: expensive, rigid platforms controlled by a few cloud providers. To address the needs of our customers and our sister companies, we modified and deployed the internal solution we had previously developed for advanced computational research, called Tela, which gave us the flexibility, cost-efficiency, and security the market lacked. That’s when the bigger picture clicked: this wasn’t a transition from one business to another—it was a consolidation of all the challenges and lessons we’d seen across industries. With MAGIC Research, our mission became clear: build AI infrastructure backed by groundbreaking research that lets organizations run their own models, on their own terms, without sacrificing speed, scale, or security. The focus is on giving companies the power to shape AI around their own workflows and goals.
2. Many teams are desirous to use AI for core operational-level tasks. What convinced you that generative AI could play a more strategic role in deep research and campaign execution?
We’ve observed that while many organizations begin with generative AI for surface-level tasks, there is a growing demand to integrate it into core operational functions. The challenge has been that companies face significant barriers — compliance, regulatory alignment, ethical use, and governance — before even addressing the technical hurdles of infrastructure, scalability, and security. Once those are solved, cost quickly becomes the decisive factor, often preventing production-ready deployment. What sets our approach apart is that we’ve taken these challenges into account from the start. By combining our research with practical deployment frameworks, we enable generative AI to move beyond experimentation and actually deliver value at the strategic level. This means we’re accelerating research and embedding AI into the campaign execution process itself — covering core competencies, ensuring compliance, and doing so in a cost-optimized, production-ready way.
3. Fabric Hypergrid originated as an internal tool. What were the specific pain points or limitations in existing solutions that led you to build your own platform from the ground up?
Fabric Hypergrid was born out of necessity. We couldn’t find an AI platform that gave us what we really needed: affordability, full customization, and true privacy. The cloud solutions on the market were expensive and locked behind someone else’s infrastructure and pricing. They weren’t built for companies that wanted to run AI privately on their own servers, or for teams that needed to fine-tune models and workflows to their specific use cases. For us and for a growing number of companies across industries — that’s a deal-breaker. Whether you’re in healthcare, finance, or media, you can’t afford to put sensitive data and critical operations in someone else’s black box. So we built Fabric Hypergrid as a distributed, hardware-agnostic AI compute platform. It lets companies run secure, scalable AI workloads anywhere they need, whether that’s on-premise, across hybrid cloud, or distributed environments. What started as an internal tool to solve our own problems has become the foundation for how other organizations build and run AI on their own terms.
4. Scaling content with depth and accuracy remains a major challenge. How does MAGIC Research ensure that generated outputs go beyond generic responses and reflect real substance?
When it comes to scaling content with depth and accuracy, we see it as a multivariate challenge—there isn’t a single lever you pull to solve it. At MAGIC Research, the core of our approach is what we call Private AI. What we’ve learned is that the single most critical factor for depth and accuracy is context. Without the right contextual layer, you’ll always end up with surface-level answers. That’s why we’ve invested heavily in building advanced, next-generation RAG systems and context engineering techniques. These allow us to extract and structure the most relevant information, so responses aren’t just generic. And then, when you combine that with robust, sophisticated agentic systems, you get outputs that consistently go beyond surface-level AI and truly deliver depth, accuracy, and real value.
5. Speed is a major benefit of AI, but so is relevance. How does the platform balance rapid output with contextual precision, especially for SEO, product messaging, or market positioning?
Speed without context is useless — you’ll just flood the world with content nobody cares about. What we focus on with Fabric Hypergrid (Private AI), MAGIC Retrieval-Augmented Generation, and MAGIC Squads (Agentic Systems) is context engineering with dynamic retrieval, precision, and targeted generation. That’s what makes the content sophisticated: the AI isn’t just fast, it’s tuned to your audience, market position, and competitive landscape. For example, if you’re building product messaging, our platform pulls live data from your CRM, customer feedback, competitive analysis, and market trends. Instead of a generic feature list, you’re getting content that reflects what customers care about right now, and adapts as those priorities shift. The Private AI and Hypergrid architecture also power autonomous agentic systems that can run multiple specialist models and queries in parallel. That creates more accuracy without forcing a tradeoff between speed and quality. In fact, our adaptive systems can test hundreds of hypotheses to converge on the best possible outcome. Speed vs. quality, the tradeoff most people face in AI, is where we stand apart. We’ve built an architecture where quality compounds over time, producing not just content but market impact.
6. Most AI platforms struggle with customization. How do Private AI and Fabric Hypergrid adapt to the unique workflows and objectives of different organizations?
That’s exactly the problem we built Private AI and Fabric Hypergrid to solve. Most AI platforms force you into a one-size-fits-all box. You can tweak prompts, but the underlying compute, models, and workflows remain rigid. Hypergrid flips that model. It’s a hardware-and software-agnostic, modular AI fabric that lets organizations run whatever models they want — open-source, proprietary, fine-tuned, multimodal — and orchestrate them seamlessly across distributed infrastructure. But customization doesn’t stop at the compute layer. On top of Private AI, we’ve built what we call the Agent Factory framework. This allows us to deploy highly tailored, agentic workflows that can reason for extended periods of time, chain together complex steps, and integrate compliance or human-in-the-loop checks directly into the process. The result is a system that adapts to your workflows and objectives, whether that means molecular simulations in pharma, fraud detection in banking, or dynamic personalization in retail. So the customization happens at every level: compute, models, data sources, governance, workflows, and outputs. That’s what makes the platform not just flexible, but truly enterprise-ready.
7. From your experience, what’s the most misunderstood aspect of integrating generative AI into marketing or research pipelines?
The biggest misconception is that generative AI is a plug-and-play magic wand. People think you just feed it some data, press go, and boom, you get perfect insights or campaigns. In reality, it can be a complex system that requires deep integration, thoughtful design, and ongoing governance. Too many teams underestimate the importance of building secure data pipelines, fine-tuning models to their domain, and layering in retrieval-augmented generation to ground outputs in real facts. They also overlook the need for human oversight and compliance controls, especially in regulated industries. What we focus on at MAGIC Research is not just the flashy output but the infrastructure that makes generative AI reliable, private, and aligned with real business goals. Without that foundation, AI outputs become noise or worse, a liability. So, the most misunderstood thing is thinking AI can replace expertise instead of augmenting it.
8. As Fabric evolved into MAGIC Research, what were some of the biggest lessons you took from your experience with other businesses that informed how you structured the new company?
As Fabric evolved into MAGIC Research, one of the biggest lessons we carried over from my experience with other businesses was the importance of solving a real and pressing customer problem. In AI, the hype can make it easy to build prototypes quickly, but the real challenge, and where companies often stumble, is in taking those prototypes to production. What we discovered very early on is that the real barriers to AI adoption aren’t about having access to models; they’re about flexibility, governance, compliance, and cost. These are the hurdles that stand in the way of democratization and large-scale adoption. We also learned that companies and organizations are often willing to trade very fast turnaround for higher-quality output. That insight reinforced the most important takeaway for us: building solutions that tackle these pain points head-on is what creates value people are truly willing to pay for. That principle to solve a painful, structural problem rather than chasing hype has been the foundation for how we’ve structured MAGIC Research.
9. With increasing demand for efficient and reliable marketing tech, what industries or business functions are you seeing the strongest pull from when it comes to adopting MAGIC Research?
Right now, we’re seeing the strongest pull from industries where both quality and accuracy are mission-critical. That’s financial services, healthcare, life sciences, energy, and increasingly, retail and consumer tech. We are seeing meaningful demand from tech startups wanting to innovate in the space with something unique and not just a wrapper around “OpenAI”. Content accuracy, uniqueness, depth, and quality are growing demands and require more advanced system implementations. These are sectors where decisions can’t be made on surface-level insights. They need deep research, compliance, and real-time market awareness. From a business function standpoint, it’s not just IT or data science teams. We’re working directly with R&D groups, go-to-market teams, strategy, and marketing leaders who need to operationalize AI across their workflows. In marketing specifically, the demand is coming from teams trying to break out of “content spam” and instead drive precision messaging, market segmentation, and competitive positioning at scale and with governance. The common thread is that they’re all hitting a wall with traditional SaaS marketing tools. They need AI that’s adaptable to their data, secure for their industry, and fast enough to matter. That’s where Fabric Hypergrid fits.
10. Looking ahead, how do you envision the role of AI in reshaping not just how companies work—but how they think, plan, and build for growth?
Looking ahead, I see AI fundamentally reshaping how companies think, plan, and grow. At its core, AI is a human augmentation tool. It’s about empowering people to achieve levels of quality, efficiency, and productivity that were simply not possible before, and doing so in a way that can even create a better balance between work and life. There’s really no reason why companies and professionals today shouldn’t be using AI to accelerate outcomes, improve quality, save time, and reduce costs. But the real opportunity goes beyond efficiency. AI has the power to change the way organizations make decisions, set strategies, and design for growth because it brings a new level of intelligence and foresight into planning. And importantly, it’s not about replacing humans. It’s about amplifying human potential — helping people do more, do it faster, and ultimately raise the standard of what’s possible in both business and life.
A quote or advice for readers “AI, when used efficiently, has the power to improve our quality of life, helping us achieve more, reduce stress, and make the most of our time. It’s not just about advancing your profession or increasing results; it’s about having a strong partner that supports everything you do, allowing you to accomplish more with less.”
Humberto Farias
Chief Executive Officer, MAGIC Research
Humberto Farias is an accomplished entrepreneur and technology leader with a deep expertise in navigating startups through the intricate challenges of growth, venture capital, and M&A activities. With a strong foundation in software development, artificial intelligence, and business intelligence, Humberto has successfully founded and led several innovative organizations, driving them to achieve their full potential.
His technical proficiency is complemented by a strategic business acumen, allowing him to steer companies across diverse industries—ranging from finance, sports, media, and entertainment to energy, education, and technology.