The problem
A large fragrance wardrobe creates an unusual form of decision fatigue. The question is no longer “Do I own something suitable?” but “Which bottle fits today’s weather, setting and desired presence?” Static spreadsheets can store the collection, but they do not help with the moment of choice.
The system
Scent OS combines collection data with context. The daily mode reads local weather and presents a suitable choice with a reason and suggested spray count. A separate discovery layer supports random exploration or context-based selection when the user wants inspiration rather than a strict recommendation.
The collection is synchronized from Google Sheets, so the interface can search across bottle name, brand, perfumer, release year, concentration and olfactive family without hard-coding every update into the page.
Layering logic
Layering formulas are organized by role rather than by random pairings. A base provides depth and persistence, a lift opens the composition, and a veil softens or connects the two. This makes each formula explainable and easier to adapt.
A collection as knowledge
Scent Worlds groups the wardrobe by atmosphere, while the Perfumer Index reveals creative signatures across houses and release periods. The result is not only a picker; it is a personal knowledge interface for understanding the collection.
What the project demonstrates
- Personal data becomes more valuable when it supports a real decision.
- Recommendations need an explanation, not only an output.
- Context, taxonomy and interface design can turn a hobby database into a useful product.