Building AI products for India means supporting Hindi, English, and regional languages. We share our approach to multilingual NLP and the challenges of code-switching.
India has 22 officially recognised languages and hundreds of dialects. Any AI product aiming for national reach must handle this linguistic diversity gracefully. At QverLabs, multilingual support is not an afterthought or a translation layer; it is embedded in our system architecture from the start.
The Code-Switching Challenge
Indian users frequently switch between languages within a single conversation, or even within a single sentence. A user might type "mujhe kal ka weather batao for Mumbai" mixing Hindi and English seamlessly. Standard NLP models trained on monolingual data struggle with this. Our models are trained on real conversational data that includes natural code-switching patterns, enabling them to understand mixed-language inputs without requiring users to stick to one language.
Beyond Translation
True multilingual AI goes beyond translating English outputs into other languages. Cultural context, idioms, and domain-specific terminology all differ across languages. Our astrology platform Staarvani, for example, uses Sanskrit astrological terms when communicating in Hindi but switches to anglicised equivalents for English conversations. Getting these nuances right is what makes the experience feel natural rather than mechanical.
Building Multilingual Systems
Our approach starts with multilingual foundation models and fine-tunes them on domain-specific data in each target language. We maintain separate evaluation sets for each language to ensure quality does not degrade in lower-resource languages. User feedback loops are critical: real-world usage reveals language patterns and edge cases that no test set can fully capture.
