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Stock problems are often hidden inside spreadsheets until something runs out—or cash is tied up in items that are not moving.
I built this editable Excel inventory dashboard to provide a clear management view of:
• Total inventory value
• Items requiring reorder
• Recommended order quantities
• Slow-moving stock
• Excess stock
• Category-level value and exceptions
The reorder points, ageing thresholds, stock records and calculations remain fully editable. This is a working Excel tool, not a static mock-up.
If your stock reporting is manual, difficult to interpret or spread across multiple files, send me a message. I can review an anonymised example and suggest what could be improved.
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In this case, we were designing Deuce a tennis analytics platform that needed to make complex match data feel immediate and spatial, not just numerical.
The brief was clear: analysts and coaches don't just want stats. They want to feel like they're watching the match while reading the data.
So we made the Wimbledon court the literal center of the interface. Every panel orbits it match overview and surface performance on the left, live scoreboard and tournament state on the right, performance trend running the full width below. The court isn't decoration. It's the anchor that makes every number feel like it belongs to a real moment on a real surface.
72.34% win rate. 48 aces. 210 km/h best serve. Djokovic leading Alcaraz 6-4 on Center Court. Alcaraz vs Sinner tonight at 11 PM. Tactic heatmaps showing where points are won and lost.
What we noticed was that spatial context changed how the data felt. The same numbers in a standard table read as analysis. Wrapped around a court, they read as intelligence.
Do you think sports UI design is still underestimating how much context and spatial layout affect how data is understood?
Nice aesthetics, but composition feels bottom-heavy.
This portfolio sample shows how I evaluate AI agents and automated workflows to find reliability problems, broken logic, and weak edge-case handling. I review whether the system completes the intended task, uses tools correctly, follows instructions, handles ambiguity, and produces an output that can actually be trusted.
The work focuses on identifying failure points before they become real user problems, including incomplete task execution, incorrect assumptions, workflow dead ends, hallucinations, and verification gaps. This is a portfolio demonstration of my AI agent evaluation and red-teaming work. No confidential client information is included.
Thorough breakdown! Robust edge-case handling and reliability checks are definitely the trickiest part of building reliable AI agents 🤖📊
This is a retail sales and profit dashboard I built from scratch to turn complex spreadsheet data into easy-to-read visuals. The main goal of this project was to make a messy dataset meaningful so a business owner can instantly check how their store is performing.
Here is how I worked on this project:
Data Cleaning: I went through the entire raw file in Excel, filtered out bad entries, and organized the rows to make sure the dashboard numbers are completely accurate.
KPI Tracking: I used Excel formulas to pull out core business metrics, such as total revenue, total orders, and the overall profit margin (12.47%).
Designing Charts: I set up clean visuals to show key trends, like sales by shipping modes, customer segments, and a breakdown of top-performing products.
I also added dynamic slicers, so you can click on any region or year and the whole screen updates automatically. I handled this entire project manually from start to finish.
Tools used: Advanced Excel (Pivot Tables, Slicers, Formulas, Dashboard Design)
