DAKSH is engineered to operate as a truly multilingual AI assistant, capable of delivering accurate, consistent, and context-aware responses across a wide range of Indian and global languages. This multilingual functionality is not an auxiliary feature but a core architectural capability — deeply embedded within both the model’s training regime and its runtime execution layers. The system is optimized to support fluid code-switching, dialect recognition, and semantic equivalence across languages, making it uniquely suited for deployment in linguistically diverse environments.
At the heart of this capability is a set of embedding alignment strategies and language-specific routing mechanisms implemented at both the encoder and decoder stages of the model. During training, DAKSH is exposed to semantically equivalent corpora across multiple languages. This bilingual and multilingual parallel data is used to align the internal representations such that similar concepts, regardless of language, are projected into a shared semantic embedding space. This ensures that when a query is posed in Hindi, Marathi, or Tamil, the model can reason over knowledge originally authored in English or another language — and vice versa.
During inference, DAKSH performs automated language detection as an early preprocessing step. Based on detected language tokens or acoustic signatures (in case of voice input), it activates the appropriate language context profile. This profile governs downstream operations such as retrieval filtering (language-matching of chunks), translation fallback (when documents are in a different language), and reranking behavior.
The architecture also incorporates a Multilingual Response Control module, which guarantees that outputs are rendered in the language of the query — even if the retrieved documents were written in another language. This is essential for user comprehension and trust, particularly in public service scenarios.
Combined, these capabilities enable DAKSH to deliver intelligent, natural interaction across urban, semi-urban, and rural deployments, bridging the language accessibility gap and supporting governance, enterprise support, and education in the user's preferred language — without compromising accuracy or response structure.