Customer Story - National Institute of Fashion Technology (NIFT)
Fashion trends evolve at lightning speed, and staying ahead requires data that performs. The National Institute of Fashion Technology (NIFT) was looking for an efficient way to process vast amounts of real-world fashion data efficiently. With IndiVillage’s AI-driven approach, NIFT turned scattered images into structured insights, helping them analyze trends at scale.
Overview
Faster, Smarter Trend Tracking
With AI managing large-scale data processing, NIFT can now access real-time fashion insights without the delays of manual analysis. The AI-driven system not only improved speed but also ensured accuracy, with even the lowest-performing model achieving 83% accuracy. By automating trend detection for our client in just 9 months, NIFT has enhanced its ability to track and predict shifts in the fashion industry with unprecedented precision and efficiency.
The Challenge
Too Much Data, Not Enough Time
NIFT’s student trend-spotters across 16 campuses were capturing thousands of images daily, documenting what people wore in different cities and seasons. However, manually sorting and analyzing this massive volume of images was overwhelming. With limited internal capacity, extracting meaningful insights from this unstructured data became a major bottleneck, delaying their ability to forecast trends accurately.
The Solution
AI Sorting, Human Oversight, Instant Insights
IndiVillage developed an end-to-end AI-powered solution, where student trend-spotters uploaded images directly to the VisioNxt platform.
From there on, 23 machine learning models processed the images, identifying 113 types of clothing with 90%+ accuracy, enabling real-time trend visualization.
A robust dataset of 2.3 million labeled images ensured AI precision, with 100+ IndiVillage experts fine-tuning the models.
Project Focus
- AI Model Development
- Image Labeling & Processing
- Platform Integration (VisioNxt)
- Real-time Trend Visualization
- Data Annotation
