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| # Shiprocket AI | |
| **AI/ML Team Building Intelligent E-commerce Solutions** | |
| ## About | |
| Shiprocket Sense is the AI/ML division within Shiprocket, focused on developing practical machine learning solutions for e-commerce operations. We build small language models and scalable deep learning systems that address real logistics and commerce challenges. | |
| Our work centers on creating efficient, deployable models that can handle the demands of production e-commerce environments while maintaining cost-effectiveness and reliability. | |
| ## Focus Areas | |
| ### Small Language Models | |
| We develop compact language models specifically optimized for e-commerce applications. These models are designed to run efficiently in production environments while delivering strong performance on domain-specific tasks. | |
| Our SLM work emphasizes: | |
| - Resource-efficient architectures suitable for real-time deployment | |
| - Task-specific fine-tuning for commerce and logistics applications | |
| - Models that balance performance with operational costs | |
| - Inference optimization for high-throughput scenarios | |
| ### E-commerce Applications | |
| Our work in e-commerce can be categorised into: | |
| **Address Intelligence**: Natural language processing for Indian address parsing, standardization, and geocoding. Our NER models handle the complexity of Indian address formats across different languages and regions. | |
| **Product Understanding**: Automated categorization, attribute extraction, and content analysis for product catalogs. This includes handling multilingual product descriptions and varying data quality. | |
| **Operational Optimization**: Return to Origin - Customer Risk prediction using predictive models and optimization algorithms. | |
| ## Mission | |
| Making machine learning practical and accessible for e-commerce operations through efficient models, robust infrastructure, and clear focus on business impact. | |
| We believe that the most valuable AI systems are those that solve real problems reliably and cost-effectively, rather than pursuing complexity for its own sake. | |
| ## You can checkout more details here - https://www.shiprocket.in/sense/ | |
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