TextileSpot
TextileSpot

TextileSpot

TextileSpot is an online platform designed to connect fashion brands and clothing manufacturers. Brands can submit requests for production, and manufacturers can publish information about their products.

The project was initially focused on creating an intuitive and user-friendly website, but as it developed, it became clear that traditional methods of searching and interaction between users limited efficiency.

To improve convenience, search speed and interaction, AI-Driven Features were implemented, which automated key processes.

Project Summary

Industry:

Fashion

Location:

UK

Technology Stack:

Python, Django

Duration:

12 months

Objective:

To create a website that can connect fashion brands and apparel manufacturers

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Business Challenges

Before launching TextileSpot, there were several key challenges:

Long search process

brands spent a lot of time searching for suitable manufacturers.

Low personalization

the site did not take into account user preferences when issuing results.

Limited filtering options

brands could only use basic filters, which did not always lead to accurate results.

Lack of user behavior analytics

the administration could not analyze which product categories were most in demand.

Solution

AI-Driven Features allowed us to implement the following:

Intelligent Search and Advanced Filtering

Instead of standard search, AI algorithms analyze user data and improve the process of selecting manufacturers:

  • The system takes into account search history and brand preferences.
  • Algorithms rank manufacturers in search results based on the most relevant criteria.
  • New intelligent filters have been added (e.g. recommendations for popular manufacturers, analysis of product characteristics).
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Personalized recommendations

TextileSpot itself suggests manufacturers to brands based on their previous requests and actions on the site.

  • If a brand is looking for sustainable materials, AI will suggest manufacturers with certified eco-friendly products.
  • If a brand is interested in producing sportswear, the system will bring manufacturers specializing in this segment to the top.
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Automated content management

Thanks to AI, the system analyzes which product categories are most in demand and:

  • Optimizes content on the site, highlighting popular categories.
  • Automatically suggests the best product descriptions, analyzing past successful publications.
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AI analytics for business

Thanks to intelligent analytics, TextileSpot understands user behavior and can:

  • Track which product categories and manufacturers are in greatest demand.
  • Identify which filters are most popular among brands.
  • Optimize the promotion strategy by analyzing customer behavior.
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Delivered Results

Cost

Cost Savings Time Saved

85

%

Faster Calculations

60

%

Engineering-Efficiency

30

%

Waste Reduction

At the end of the project, both front and back-end parts were ready, layout correlating to the design we had and all the functionality working efficiently.

 

Both brands and manufacturers could find something for themselves on an intuitive and visually appealing website that could connect them with each other and thus, advance their ideas about future products and their manufacturing.

 

Results of implementing AI-Driven Features

 

  • Search for manufacturers has become 50% faster – AI has significantly accelerated finding the right suppliers.
  • Conversion has increased by 35% – personalized recommendations have increased brand interaction with manufacturers.
  • The site has become more adaptive – thanks to smart filters and content management, updating the platform has become easier.
  • Analytics have improved by 60% – AI has identified key trends and user preferences.
  • User engagement has increased by 40% – thanks to an improved recommendation system.

 

The implementation of AI-Driven Features has allowed TextileSpot to become an intelligent platform where brands and manufacturers find each other faster, more accurately and more conveniently.

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