Beyond Models: Building AI Systems with Everson
Everson Luz | Advisor, Project/Program Management | Brazil
Originally Published: January 28, 2026
I am part of Dell’s Research team, where I build the operational backbone that turns advanced AI into reliable, scalable, and audit-ready outcomes. With a hands-on IT foundation built earlier in my career and experience as a Technical Support Engineer after joining Dell in 2019, I transitioned into the Research team in 2023. My focus is reducing friction at scale—standardizing data flows, integrations, and automations—so researchers can spend more time innovating. I use persona-driven testing and Dell’s internal AI tools to anticipate user needs, resolve ambiguity (such as differences in how status or stage fields are interpreted), and preserve traceability and compliance. Guided by a “team first” mindset and a culture that shows up in practical ways, I aim to build stable systems that help my colleagues shine and enable ideas to move forward faster.
What AI-related project have you worked on that makes you feel most proud, and why?
The AI-related work I am most proud of was not a single model, but the design of an end-to-end system that makes AI research more reliable, scalable, and usable in real-world environments. As research advanced, operational friction began to drain energy from innovation: multiple data sources, high volumes, cross-team dependencies, and strict traceability requirements made everything slow and inconsistent.
My challenge was simplifying the process without compromising the integrity of the data. I built scripts, automations, and integrations that run in just a few clicks, reduce ambiguity, and produce audit-ready outputs that anyone can understand—without opening code.
What makes me proud is the quiet impact: researchers now have the mental space to focus on what matters most—experimentation, discovery, and innovation. My goal was never to stand out, but to help the team shine, and this system does exactly that.
How does AI help you anticipate user needs and reduce errors during development?
AI became my continuous beta tester. I used Dell’s internal AI tools to build personas for researchers and stakeholders, then simulated their questions, behaviors, misunderstandings, and likely mistakes. This revealed issues that traditional engineering often overlooks—for example, the same “status” field interpreted as a priority by some users and as a workflow stage by others. That small ambiguity can create major rework at scale. By testing messages, flows, and output formats through these personas, I refined clarity, reduced confusion, and validated my decisions before automation ever reached the hands of the team. AI allowed me to anticipate user behavior early, ensuring smoother adoption and fewer errors later.
How has your career journey influenced the way you approach AI and operational design at Dell?
My journey at Dell has shaped everything about how I design systems. I started with a hands-on IT background, coordinating operations and keeping environments stable under pressure. In 2019, I made what looked like a step backward on paper—I joined Dell as a Technical Support Engineer to get closer to engineering and real-world problems.
Support wasn’t the end goal; it was the foundation. It taught me how users think, how systems break, and how clarity impacts outcomes. The culture at Dell also played a defining role and seeing firsthand what “team first” really means.
When I joined the research team in 2023, everything connected: engineering, operations, problem solving, and a culture that invests in people. Today, I use my diverse background to turn complexity into clear, stable, sustainable processes so researchers can innovate without friction. It’s like strength training—growth comes from consistency, a solid base, and steady progress over time.
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