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.

Spaces:

shiprocket-ai
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README

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