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Purpose-built AI systems for the industries we serve.
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AI-native search that understands natural language and delivers direct answers
Multi-tenant AI CRM with a per-industry module architecture — each new industry gets a tailored feature set on shared infrastructure
AI orchestration layer coordinating agent workflows across CRMs, email, and marketing tools
Business analytics platform for data-driven decisions
Practice management software with AI-powered patient communication
Autonomous AI agents for ecommerce buying journeys
Intelligent scheduling that handles complex availability
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Nepali Language AI: Building Chatbots & Search in Nepali
Technical guide to NLP for the Nepali language
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The challenge of Nepali NLP
Building AI systems that understand Nepali presents unique challenges. Nepali is a low-resource language with limited training data compared to English or Chinese. The Devanagari script has complex character combinations. And users naturally mix Nepali and English in queries (code-switching).
This guide covers practical solutions for these challenges, drawing on our experience building AI systems for the Nepal market. Whether you're creating chatbots, search systems, or document processing pipelines, this technical guide will help you navigate the complexities of Nepali NLP.
Key challenges we address
Devanagari script handling
Unicode normalization, character combinations (संयुक्ताक्षर), and text preprocessing for ML pipelines.
Code-switching detection
Handling queries like "yo product ko price kati ho?" that mix Nepali and English in the same sentence.
Low-resource language strategies
Transfer learning from Hindi, multilingual models, and data augmentation techniques.
Tokenization and embeddings
Word segmentation, subword tokenization, and creating embeddings for Nepali text.
What you get:
Frequently asked questions
Yes, modern LLMs have some Nepali capability, but performance varies. We cover prompt engineering techniques specific to Nepali, when to use LLMs vs. specialized models, and how to evaluate Nepali language performance.
The guide covers available Nepali datasets (newspapers, Wikipedia, social media), data augmentation strategies, and how to create your own training data efficiently.
Primarily practitioners. We focus on production-ready solutions rather than academic exploration. That said, we reference relevant research and provide pointers for those who want to go deeper.