Nimbèa — AI Wardrobe Intelligence

Nimbèa is an independent AI-powered product currently under development, designed to help people understand, organize and make better use of the clothes they already own.

The product combines wardrobe intelligence, computer vision, personalized recommendations and contextual styling to turn a digital wardrobe into an intelligent decision-support system.

Independent product · Currently under development

The Problem

Most digital wardrobe experiences focus primarily on cataloguing clothes.

Nimbèa is being developed around a broader question:

How can technology help people make better everyday clothing decisions using the wardrobe they already own?

Nimbèa connects wardrobe data, garment attributes, personal preferences and contextual information to support more relevant and personalized outfit decisions.

Product Capabilities

Nimbèa is being developed around several interconnected capabilities:

• Digital wardrobe management
• AI-assisted garment classification
• Computer vision for garment understanding
• Outfit creation using items from the user's own wardrobe
• Personalized outfit recommendations
• Style and preference modeling
• Context-aware recommendations
• Wardrobe intelligence
• Batch wardrobe ingestion
• Product and purchase data integration

Product Development Approach

Nimbèa is developed iteratively, starting from real user problems and translating them into product requirements, user flows and technical solutions.

The development process combines:

• Problem definition and user needs
• Product requirements and prioritization
• User experience and workflow design
• Technical feasibility assessment
• AI experimentation and evaluation
• Implementation and integration
• Testing and iteration
• Continuous refinement based on product and technical learnings

AI & Technology

Nimbèa integrates AI and computer vision into the product experience to transform unstructured garment images and user preferences into structured wardrobe data and personalized recommendations.

The product development work includes:

• AI-assisted garment detection and classification
• Garment attribute extraction
• Computer vision workflows
• Image processing and analysis
• Structured wardrobe data
• Personalized recommendation logic
• AI-powered product experiences
• Evaluation and integration of AI capabilities into application workflows

Product & Engineering Decisions

Nimbèa involves evaluating different technical approaches based on user experience, product requirements, feasibility and scalability.

Examples include:

• Choosing different processing strategies depending on the workflow and volume of data
• Designing structured data flows between product features and AI-enabled capabilities
• Separating user-facing workflows from asynchronous processing where appropriate
• Designing review and confirmation steps before AI-detected items are committed to the user's wardrobe
• Balancing automation with user control in AI-assisted experiences

Architecture

Nimbèa is built as a modular web application combining a user-facing product layer, application logic, persistent data storage and AI-enabled processing workflows.

At a high level, the architecture connects:

User Experience → Application Logic → Data & Storage → AI Processing → Structured Wardrobe Intelligence → Personalized Recommendations

High-Level Architecture

┌─────────────────────────────┐
│        User Experience      │
│                             │
│  Wardrobe · Outfits · Chat  │
│  Recommendations · Profile │
└──────────────┬──────────────┘
               │
               ▼
┌─────────────────────────────┐
│      Application Layer      │
│                             │
│ Product workflows           │
│ User preferences            │
│ Business logic              │
│ Recommendation workflows    │
└──────────────┬──────────────┘
               │
       ┌───────┴────────┐
       ▼                ▼
┌──────────────┐  ┌──────────────────┐
│ Data &       │  │ AI Processing    │
│ Storage      │  │                  │
│              │  │ Image analysis   │
│ Database     │  │ Garment detection│
│ User data    │  │ Classification   │
│ Wardrobe     │  │ Attribute        │
│ Images       │  │ extraction       │
└──────┬───────┘  └────────┬─────────┘
       │                   │
       └─────────┬─────────┘
                 ▼
┌─────────────────────────────┐
│    Structured Wardrobe      │
│        Intelligence         │
│                             │
│ Garments · Attributes       │
│ Preferences · Context       │
└──────────────┬──────────────┘
               │
               ▼
┌─────────────────────────────┐
│ Personalized Experiences    │
│                             │
│ Outfit Recommendations      │
│ Styling Assistance          │
│ Context-aware Suggestions   │
└─────────────────────────────┘

This approach allows AI-generated information to be reviewed and incorporated into the user's wardrobe while maintaining user control over the final data.

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Product Development Principles

Nimbèa is being developed with a focus on:

• User value over technology for its own sake
• Practical application of AI to real product problems
• Human oversight in AI-assisted workflows
• Structured and reusable product data
• Iterative experimentation
• Maintainable product architecture
• Scalable data and processing workflows
• Continuous improvement of the user experience

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Current Status

Nimbèa is an independent product currently under active development.

The product is being iterated across product experience, AI capabilities, computer vision, data architecture and application workflows.

This repository documents the ongoing development process, selected product decisions and technical implementation.

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Demo

Open Nimbèa

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Project Structure

The repository includes the main application source code, backend/data components and supporting documentation.

Key areas include:

• src/ — application source code
• supabase/ — backend and database-related components
• docs/ — supporting project documentation

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About the Project

Nimbèa is an independent product project exploring how AI, computer vision, structured data and product design can work together to create more intelligent and personalized user experiences.

The project combines product thinking, user experience, business problem solving and hands-on technology development.