Aurafit

Aurafit is an AI personal stylist that creates personalized outfits using real products you can actually buy, based on your appearance, occasion, budget, and style preferences.

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Inspiration

Choosing what to wear is a daily challenge that affects millions of people. Whether it's a job interview, a date, a wedding, or simply deciding what to wear every day, people often spend significant time trying to find outfits that suit them.

While many fashion applications provide generic recommendations or generate AI-created outfits that don't actually exist, we wanted to build something practical. Our goal was to create an AI personal stylist that understands the user, finds real products available online, and helps them make confident fashion decisions.

That's how Aurafit was born.

What it does

Aurafit is an AI-powered personal styling platform that helps users discover outfits tailored to their appearance, preferences, occasion, and budget.

Users upload a photo, select an occasion, define their budget, and choose their preferred style. Aurafit analyzes the user and generates multiple personalized outfit recommendations.

Unlike traditional fashion AI tools, Aurafit goes beyond visual inspiration. It discovers real products from online retailers, assembles complete outfits using those products, and provides direct shopping links for every item in the recommendation.

Aurafit also includes wardrobe intelligence features that allow users to upload clothes they already own, receive matching outfit suggestions, and discover complementary products that fit their existing collection.

How we built it

Aurafit was built as a full-stack AI-powered fashion intelligence platform.

The frontend was developed using Next.js, React, TypeScript, Tailwind CSS, and Framer Motion, while the mobile application was built using React Native and Expo.

Our backend leverages PostgreSQL, pgvector, Redis, Azure Storage, and Azure OpenAI to manage user profiles, image analysis, recommendation workflows, vector search, and outfit generation.

To power real-world product discovery, we built an agentic product intelligence pipeline using Playwright and Crawl4AI. This system searches the live web, discovers relevant fashion products, extracts product data, and assembles complete purchasable outfits based on the user's requirements.

The platform combines computer vision, recommendation systems, product discovery, AI reasoning, and image generation into a unified personal styling experience.

Challenges we ran into

One of the biggest challenges was preserving personalization across generated recommendations. We wanted every outfit to feel tailored to the user rather than generating generic fashion suggestions.

Building a scalable architecture for user profiles, wardrobes, generated outfits, and recommendation history was another significant challenge. The platform needed to support growth while maintaining performance and cost efficiency.

Product discovery presented additional difficulties. Many fashion websites actively prevent traditional scraping techniques, requiring us to experiment with multiple crawling and extraction approaches to maintain reliable product coverage and recommendation quality.

Balancing recommendation quality, generation speed, infrastructure costs, and user experience required continuous iteration throughout development.

Accomplishments that we're proud of

We successfully built a working AI personal stylist available across both web and mobile platforms.

We created a system that generates personalized outfit recommendations using real products instead of fictional AI-generated clothing.

We developed an intelligent product discovery pipeline capable of finding relevant fashion products and assembling complete outfits with direct shopping links.

Most importantly, we transformed a common everyday problem into a practical product that users can immediately benefit from.

What we learned

Building Aurafit taught us that creating a successful AI product requires far more than integrating a language model.

We gained valuable experience in cloud infrastructure, scalable system design, recommendation systems, computer vision, vector databases, product discovery, and production deployment workflows.

We also learned the importance of designing around real user needs rather than focusing solely on technical capabilities. Delivering a useful and reliable experience required combining multiple technologies into a cohesive product that solves a genuine problem.

Most importantly, we learned that the most impactful AI products are those that bridge the gap between intelligence and action.

What's next for Aurafit

Our vision is to make personal styling accessible to everyone.

We plan to expand our product discovery network, improve wardrobe intelligence, introduce more advanced personalization, and support additional fashion categories and retailers.

Future versions of Aurafit will include smarter style memory, trend-aware recommendations, collaborative styling experiences, and deeper integrations with shopping platforms.

Our long-term goal is to build a true AI personal stylist that helps users save time, shop smarter, and feel more confident in what they wear every day.

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