README.md

# Shiprocket AI

**Speech AI and Machine Learning Systems for Commerce**

Shiprocket AI is the applied AI/ML team behind **Shiprocket Sense**, focused on building production-ready models for **speech, language, and logistics intelligence**.

Our work combines **Speech AI, Small Language Models, and predictive machine learning systems** to power real-world e-commerce and logistics workflows across India.

We focus on building **efficient models that can run reliably in production**, supporting millions of commerce transactions and logistics operations.

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# Core Focus Areas

## Speech AI (TTS & STT)

We are building multilingual **Speech AI systems** designed for real-world commerce applications such as conversational agents, customer support automation, and voice-driven workflows.

### Text-to-Speech (TTS)

Our TTS systems generate **natural, expressive speech** optimized for real-time conversational systems.

Key areas of development:

- Multilingual speech synthesis for Indian languages
- Low-latency inference for real-time voice agents
- Voice models optimized for customer support interactions
- Efficient training pipelines for large-scale speech datasets
- Accent and dialect adaptation for regional speech patterns

Applications:

- AI voice agents
- Customer support automation
- Conversational commerce
- Logistics communication systems

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### Speech-to-Text (STT)

We build **robust speech recognition systems** designed for noisy real-world environments such as call centers and telephony audio.

Key capabilities:

- Streaming speech recognition
- Code-mixed speech recognition (Hindi-English and other combinations)
- Telephony audio optimized models
- Domain adaptation for logistics and commerce terminology
- Efficient deployment on CPU and GPU environments

Applications:

- Call center transcription
- Conversational AI pipelines
- Voice analytics
- Automated workflow triggers from speech

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## Small Language Models (SLMs)

We develop **efficient transformer-based language models** specialized for commerce and logistics tasks.

Our SLM work focuses on:

- Resource-efficient architectures
- Domain-specific fine-tuning
- Low-latency inference
- High-throughput production deployment

These models support a wide range of NLP applications across the Shiprocket ecosystem.

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# AI Systems for E-commerce Operations

## Address Intelligence

Indian addresses are highly unstructured and multilingual.  
We build NLP systems that extract structured information from raw addresses.

Capabilities include:

- Address entity extraction (NER)
- Address normalization and standardization
- Pincode inference
- Geolocation mapping
- Multilingual address parsing

These systems power address validation and checkout intelligence APIs.

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## RTO Prediction (Return to Origin)

**Return to Origin (RTO)** is a major operational challenge in Indian e-commerce, especially for **Cash-on-Delivery (COD)** orders.

An RTO occurs when a shipment cannot be delivered and is returned to the seller, resulting in significant logistics costs.

Our RTO prediction models help merchants:

- Identify high-risk orders before shipping
- Reduce failed deliveries
- Minimize logistics losses
- Improve delivery success rates

The models use signals such as:

- Customer behaviour patterns
- Address quality and completeness
- Historical delivery success
- Order characteristics
- Regional delivery trends

By predicting RTO risk in advance, sellers can take actions such as:

- Order confirmation calls
- Switching to prepaid payments
- Address verification
- Courier partner optimization

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## Product Understanding

We build machine learning systems that help structure and understand large product catalogs.

Applications include:

- Product category prediction
- Attribute extraction
- Catalog normalization
- Multilingual product understanding

These systems help improve search, discovery, and catalog quality across e-commerce platforms.

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# Mission

Our mission is to build **practical AI systems that solve real operational problems in commerce.**

We focus on:

- Efficient model architectures
- Scalable infrastructure
- Reliable production systems
- Measurable business impact

Instead of pursuing large general-purpose models, we prioritize **specialized AI systems that deliver real-world value at scale.**

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# Learn More

More about Shiprocket Sense:

https://www.shiprocket.in/sense/

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![Shiprocket Sense](https://sr-website.shiprocket.in/wp-content/uploads/2024/11/sense-1.svg)