{DESCRB}
Case Study
Our collaboration with {descrb} focused on enhancing online shopping experiences through the development and integration of AI models for image recognition and processing. The primary objective was to reduce the time required to prepare a product page from 30 minutes to an absolute minimum, thereby accelerating the speed at which products could be introduced to the market. Initial efforts using GPT manually reduced this time to 5 minutes per product page, but the goal was to fully automate the process using AI tools.
Key aspects of the project
Technical Challenges
Developing and implementing AI solutions to streamline the online purchasing process.
Cost Savings
Reducing the manual effort involved in product page creation, thereby cutting down operational costs.
Project Results
Significant improvements in conversion rates and site traffic, enhancing {descrb}'s market position.
Technical challenges
- Object Segmentation: Utilizing a custom-tailored version of the Segment Anything technology for personalized object segmentation.
- Object Classification: Developing a proprietary training model specifically for this project.
- Brand/Logo Detection: Building a model based on our own data and algorithms.
- Text Recognition (OCR): Using an advanced version of Keras-OCR for accurate text extraction.
- Product Description Generator: Employing the GPT-J NEO 6B model and the latest LAMA models for generating detailed product descriptions
- Visual Identification: Integrating with Gemini Vision AI and Google Lens for product identification and verification using a photo comparison algorithm.
- Content Generation: Utilizing ChatGPT 3.5 to create product descriptions, features, main technologies, history, and brand essence.
Problems and solutions
Problem
The initial manual approach using GPT reduced the time to 5 minutes per product page but was still labor-intensive.
Solution
Fully automating the process using advanced AI models, thereby minimizing manual intervention and further reducing the time required for product page creation.
Project implementation stages
- 1
Initial Phase
Identifying the key areas for improvement and setting clear objectives for the AI models.
- 2
Development Phase
Creating and training the AI models for object segmentation, classification, brand/logo detection, and text recognition.
- 3
Integration Phase
Integrating the AI models with existing systems for seamless operation.
- 4
Testing and Optimization
Conducting rigorous testing to ensure accuracy and efficiency, followed by optimization based on feedback.
- 5
Deployment
Rolling out the AI solutions across {descrb}'s platform and monitoring performance.
Team involvement
- Python Developers
- Machine Learning Engineers
- QA Engineers
- Front-end Developers
- MLOps Engineers
- Project Managers
Applied Technologies
- Object Segmentation: Segment Anything technology.
- Object Classification: Custom training model.
- Brand/Logo Detection: Proprietary data and algorithms.
- Text Recognition (OCR): Advanced Keras-OCR.
- Product Description Generator:GPT-J NEO 6B and LAMA models.
- Visual Identification:Gemini Vision AI and Google Lens.
- Content Generation: ChatGPT 3.5.
Benefits
- Conversion Increase: {descrb}'s clients reported a conversion increase of up to 25%.
- Site Traffic: A 10% increase in site traffic was observed.
Conclusion
This collaboration not only strengthened {descrb}'s market position but also set new standards for future innovations in the global e-commerce sector. The project demonstrated that advanced AI and GPT technologies could define a new era in electronic commerce, significantly enhancing operational efficiency and customer experiences.
