At the input processing layer, DAKSH utilizes a proprietary tokenizer that is explicitly designed to handle the demands of multilingual, structured, and domain-specific interaction. Unlike standard tokenizers used in general-purpose LLMs, which often struggle with language mixing, non-standard formats, or contextual ambiguity, DAKSH’s tokenizer is architected with enterprise reasoning in mind — prioritizing interpretability, semantic clarity, and compatibility with retrieval-augmented generation workflows.
One of its core capabilities is the segmentation of cross-lingual inputs into optimized subword and token units. This allows the system to accurately represent diverse input streams, including those containing a mixture of English and Indian languages, regional dialects, or localized terminologies. By encoding these inputs with granular control, the tokenizer ensures that no semantic nuance is lost during downstream processing.
The tokenizer also embeds language identity and domain-aware tags directly into the input stream. This enables the model to recognize whether a phrase belongs to legal, financial, educational, or municipal domains — and apply the appropriate attention or routing mechanisms during inference. For example, queries referencing fee charts, case references, or compliance clauses are tokenized with structural and semantic cues that improve both retrieval accuracy and response grounding.
In addition to linguistic inputs, the tokenizer is designed to handle structured artifacts such as tables, lists, field-value pairs, and embedded references. These components are treated as distinct token types with positional and relational metadata preserved, enabling the model to understand hierarchical formats and generate structured outputs accordingly.
A typical processed input might look like:
javascriptCopyEdit<QueryLanguage=Hindi> <Context> ...Relevant Chunks... </Context> <UserQuery> "किस्तों में भुगतान कैसे करें?"
This encoding structure enforces a clear boundary between retrieved memory, system-defined prompt content, and user-supplied queries — allowing the model to preserve contextual integrity and differentiate between auxiliary context and active intent. It also aids in output post-processing, enabling DAKSH to return logically segmented and traceable responses, regardless of input complexity or language diversity.
This tokenizer forms the foundational layer upon which DAKSH’s structured, multilingual, and retrieval-sensitive intelligence operates — making it an essential enabler of the platform’s enterprise-grade performance.