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+# Shiprocket Sense
+
+**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 models solve specific problems in the e-commerce domain:
+
+**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.