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Kyle Chaplin
Translating ambiguity into clarity.
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One of the more interesting challenges in payroll is making financial obligations feel immediately understandable. Organization admins don’t just need to know what’s due—they need confidence that payroll is covered, visibility into what’s driving the total, and a clear next step when it isn’t. This concept explores that idea by turning payment obligations into a single, actionable experience: surfacing funding status at a glance, breaking down where costs come from, and providing AI-powered answers to the questions admins are most likely to ask. The goal wasn’t to add more data, but to reduce uncertainty around one of the most critical moments in the payroll lifecycle.
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I wanted to see how far I could push simplicity. Instead of asking merchants to interpret dozens of charts and KPIs, the experience starts with a single health score that quickly communicates the current state of the business. From there, supporting metrics provide just enough context before guiding attention to the one issue that matters most. The goal was to reduce cognitive load without sacrificing confidence in the data.
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Spent some time exploring what happens when a UI is built around uncertainty instead of certainty. Inspired by grounded sci-fi, I wanted to create a research interface where every scan uncovers something new, but never enough to fully explain what you’re looking at. Rather than leaning into horror or flashy visuals, the focus was on making the experience feel like believable scientific software. Methodical and just a little unsettling.
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Predictive Reconstruction Engine
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