Daily.Dresser данные
Источники данных, обработка и гайдлайны
MEDIA CONCEPT
Daily Dresser (Dd) — a media stylist capable of suggesting an outfit in one minute based on the weather, event, budget, and mood, focusing not on fashion as a concept, but on clothing comfort and situational context.
☆We live in an environment that increasingly dictates our choices. Marketplaces suggest what to buy, algorithms decide what to watch, and social feeds define what is beautiful and trendy. All of this introduces stress and tension into daily life. Often, people stand before a closet, unable to decide what to wear to look appropriate while still feeling comfortable.
But what happens when choosing clothes stops being a torment and becomes a moment of ease?
Weather, an appointment, mood, budget—these are no longer reasons for an hour of endless scrolling; they are input data for a complete solution. The look is constructed not from fleeting trends, but from context: who you are today, where you are going, and what the weather is like outside.
The Project’s Core Goal — to free users from the daily stress of «what to wear» by providing a ready-made solution in just one minute: tailored to the weather, event, budget, and mood.
☆Daily Dresser strives to make clothing selection more practical, compact, and swift, ensuring people are always dressed appropriately for the weather and in line with their own definition of elegance.
The goal is to transform the everyday act of choosing clothes from a routine into a swift ritual.
It is important to note that the distribution strategy is structured as a funnel: short-form content attracts attention → Telegram retains the audience → the website provides a ready solution and a deep engagement experience. Reels / VK Clips / TikTok—the entry point.
For the analysis of guidelines, sources, and data processing in this project, we will primarily examine the Telegram channel and the Daily Dresser website.
This approach was chosen to more accurately delineate the roles of the two platforms: the website serves as the product core, where input data is gathered (city, event, budget), the look is generated, and the editor reviews it; the Telegram channel functions as the publication and feedback platform (reactions, polls, comments). This allows us to demonstrate the full data workflow—from the initial request to weather databases, through to the publishing of the outfit card and the logging of selections—across two related yet functionally distinct platforms.
Furthermore, focusing on these two channels aligns with the project’s launch phase: the website and Telegram are the initial platforms where the model will function at full capacity.
DATA SOURCES
Daily.dresser uses three types of data sources: weather, user, and content.
Weather Data:
The primary external source is weather APIs (OpenWeatherMap or similar). These provide ambient temperature, perceived temperature, humidity, wind speed, precipitation type, and amount. This is «raw» data, which is automatically gathered and requires no human intervention during the collection phase.User Data:
The user provides the context: city, event (work, date, walk), budget, and mood. Studies show that even a small amount of psychographic, demographic, and physical data increases the accuracy of recommendations in fashion systems. Additionally, the system remembers past choices and preferred looks.Content Data:
Databases of clothing, looks, palettes, and explanations of «why it works.» This is editorial content that is created manually.Daily.dresser doesn’t build its image solely on big data; the product is also built on small, precise data points. The weather API provides objectivity, user data provides context, and the editorial base provides taste. None of these sources solves the problem on its own; the power lies in the fact that they converge at a single point—the specific query of a specific person.
MACHINE PROCESSING
Data Collection
Automatic monitoring of open digital platforms and specialized services. Sources include weather APIs (OpenWeatherMap and similar services), marketplaces (Wildberries, Lamoda, Ozon), Pinterest boards featuring visual inspiration, and Telegram channels of competitors and stylists—specifically those that publish finished looks, color palettes, and item descriptions (Dd does not publish other people’s looks, texts, or collections. Data from public channels is used only for trend analysis, not for content replication).The automatic request to the weather API (OpenWeatherMap or similar) occurs every 3 hours. The system retrieves temperature, perceived temperature, humidity, wind speed, and the type and amount of precipitation—and saves this data to the database, linked to the city and time. This is the only external source that is entirely automatic and does not require human intervention during the collection phase.Initial Image Generation
The specialized AI model receives three inputs: weather data from an API, user input (city, event, budget, mood), and a database of styles. Based on these inputs, the model generates 1–3 look options—each complete with a card (3–5 items, 3–4 color palette) and two lines explaining «why it works.» Generation takes 5–10 seconds. This is a draft, not the final product: it is sent to an editor for review and refinement.Logging
The system automatically saves which image a user selected on the site, what reactions a post received in Telegram, and what queries are recurring. This data is not published and does not affect real-time generation—it is needed by the editor for weekly analysis: what is working, what is outdated, and what to add to the database.Initial Filtering
Processing the gathered information based on key parameters: season, temperature range, and event type (work, date, outing). The system filters out items and looks that do not match the current request, leaving only those that fit the target audience’s desired range, weather indicators, and specified occasion.Data Structuring
Categorization of the found information: top, bottom, shoes, accessories; by season; by event; by color palette. Duplicate removal, merging item data from various sources (e.g., the same item on Wildberries and Lamoda), and formation of a structured lookbook database.Initial Card Generation
Automatic creation of look profiles. Each card includes: 3–5 items, a 3–4 color palette, a brief description explaining «why it works, ” links to similar styles, and sources for the image components. The context is saved for every outfit: what weather, what event, and what season.HAND CRAFTED
