Rising entrepreneur and problem solver. I work across AI systems, product strategy, and backend engineering—usually at the stage where things need to be figured out from scratch.
Workflow design, orchestration, and applied intelligence
Zero-to-one strategy, discovery, and execution
Backend architecture, payments, and infrastructure
Products, platforms, and systems built across marketplaces, AI infrastructure, and enterprise strategy.
Built and led a student-only marketplace shaped around trust: payments, handoffs, messaging, and the operational logic required for peer-to-peer commerce to hold together in the real world.
Built an AI research and decision platform for crypto trading, unifying charting, indicator management, strategy execution, backtesting, walk-forward validation, and promotion-gate governance into a single operator workflow.
Built a production-grade AI application engine combining deterministic ranking, async job ingestion, resume tailoring, browser automation, and human-in-the-loop review into a single operating workflow.
Led a Walmart-sponsored product strategy initiative focused on a difficult internal question: how associate feedback becomes something trusted, legible, and actionable inside large systems.
Led customer discovery and product-definition work for nonprofit financial services, translating fragmented operational pain points into a clearer product and go-to-market direction.
Technical product builder based in Fayetteville, AR, working at the edge of AI systems, product strategy, and software engineering—where ambiguity is high, constraints are real, and the right solution is rarely obvious at first.
A Computer Science degree at the University of Arkansas is being finished alongside the work of building real products. At Swaply, a marketplace moved from concept into a living system—trust, payments, real-time communication, and the operational logic required for people to actually depend on it.
The deeper interest is not only in building features, but in shaping the value beneath them: the invisible architecture that determines whether a product earns trust, survives contact with reality, and becomes useful in a lasting way. That thread runs through human-centered product strategy work with Walmart, nonprofit fintech design with Arvest, and AI platforms built for decision-making and automation.
The most meaningful work tends to live where the path is unclear—where technical depth and judgment matter at the same time. Problems worth pursuing are usually the ones that resist easy answers, demand patience, and reveal their structure only after sustained attention.
The deepest work begins beneath the surface—in incentives, fragility, and hidden dependencies. Products rarely fail because of one missing feature; they fail when the purpose underneath them is not fully understood.
Clarity is earned through contact with reality. Software becomes honest when it meets users, constraints, and consequences in the real world.
Work moves across User experience, backend, mobile, payments, and infrastructure to keep the whole intact. Better decisions emerge when strategy and implementation stay close to each other.
Most product problems do not begin at the feature layer. They begin deeper, in the structure of the system itself. What looks like a simple interface decision is often downstream of incentives, incomplete information, trust asymmetries, operational constraints, and technical tradeoffs that quietly shape the behavior of everyone involved.
Good product work starts by making those forces visible. The goal is not to over-intellectualize the problem or delay execution, but to avoid building something polished that fails the moment it touches reality. The strongest systems usually emerge from a clear mental model first, then become sharper through direct contact with users, edge cases, and the pressure of real use.
That becomes even more important in AI systems, where the surface can appear convincing long before the underlying behavior is dependable. Demos create confidence too early. In practice, the real product is rarely just the model. It is the surrounding architecture: evaluation, orchestration, fallback paths, visibility, control, and the deliberate decision of where automation should end and human judgment should remain.
The work that feels most worthwhile tends to live in that tension. Not just making systems more capable, but making them more legible, more trustworthy, and more resilient under imperfect conditions. That is usually where the real product begins.
How trust systems can hold when real incentives, asymmetry, and risk begin to distort behavior in peer-to-peer markets
What the right abstractions are for multi-step AI workflows once orchestration, evaluation, fallback, and control all have to coexist
Why so many models deteriorate in production, and which evaluation patterns remain honest after the data stops being clean and static
How human-in-the-loop systems should feel when judgment is treated as a design constraint rather than an afterthought
If you're building something ambitious and need someone who can think across product, engineering, and AI systems—let's talk.
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