A Retail Apparel Transformation with NLP and Generative AI


Client is a Texas, Dallas based retail apparel company with over 50 years in the industry. The company designs and manufactures high end fashion apparel with licensing attached to the products. Company’s apparel designers monitor fashion industry trends in designing trendy apparel and their manufacturing team out of India creates the products based on the design.
The company’s products are sold direct to consumers via the company web site and also to large retail customers like Walmart, Target etc.

Tech environment

1 . Google BERT based NLP/Open AI

2 . Snowflake deployment on top of Azure cloud

3 . Azure OpenAI (client secured environment)

4 . Python

5 . UI based on StreamLit and React.JS


The client has been using spread sheets and manual mechanisms to track product demand and maps them to existing inventory. This created huge inefficiencies due to lack of a centralized digital platform. In addition, the client was not able to capitalize on consumer trends in real- time due to a lack of required automation and technological skills within the team.
The client’s reliance on spreadsheets and manual processes to track product demand and inventory led to significant inefficiencies. These inefficiencies not only hampered their ability to meet customer demand promptly but also resulted in the production of unsold products, leading to high inventory costs and reduced profitability.


The proposed solution addressed these challenges through a multi-faceted approach:

1 . Creating a Unified Data Model: We established a common data model that mapped product attributes across sales and inventory data, providing a centralized view of product information.

2 . NLP Engine with BERT: Utilizing Natural Language Processing (NLP) powered by Google’s BERT, we analyzed customer reviews and comments on Amazon’s online store, extracting valuable insights.

3 . Automated Product Attributes: We automated the extraction of product names and attributes based on Amazon searches, seamlessly mapping them to the client’s products and inventory.

4 . Identifying Trends and Competitive Insights: Our Solution identified key consumer fashion trends and highlighted gaps in the client’s offerings compared to competitors.

5 . Next Best Action Recommendations: Based on consumer demand analysis, we provided actionable recommendations to optimize product offerings


The implementation of our solution yielded impressive results:

1 . 80% Reduction in Manual Efforts: Automation significantly reduced the need for manual data tracking and analysis, allowing the client’s team to focus on more strategic tasks.

2 . Approx. $2.5 Million in Cost Savings: The streamlining of processes and improved demand-sensing capabilities translated into substantial cost savings, enhancing profitability.

3 . Reduced Demand Sensing Analysis Time: What used to take months for demand analysis now takes just days, enabling the client to respond swiftly to market trends and consumer preferences.

“Predactica’s Generative AI platform helped us automate and extract insights into consumer apparel preferences in the ever-changing fashion industry.
What used to take us weeks in the past through manual efforts has been reduced to hours with Predactica’s Solution”

Director of Analytics

Fashion retail client