The architecture of DAKSH is meticulously designed to meet the demands of modern enterprise knowledge workflows, where information is often context-bound, multilingual, structured, and sensitive. Traditional large language models, typically trained on general-purpose internet corpora, often fall short in delivering accurate, grounded, and compliant responses when applied in such environments. In contrast, DAKSH is purpose-built with architectural priorities that address these limitations head-on.
A key objective of DAKSH is enterprise adaptability — the ability to understand and respond to queries drawn from structured and semi-structured domains such as regulatory manuals, service protocols, policy documents, and tabular datasets. Unlike monolithic black-box systems, DAKSH integrates tightly with enterprise repositories and understands domain-specific terminology, document structure, and procedural context.
To maintain retrieval sensitivity, DAKSH is architected with a Retrieval-Augmented Generation (RAG) pipeline. At its core, this enables the system to dynamically incorporate context-relevant knowledge from a vector store during each inference cycle. Whether responding to HR queries or financial compliance requests, the system ensures that outputs are factually grounded in indexed enterprise content — significantly reducing hallucination and enhancing response accuracy.
Another cornerstone of DAKSH is secure, private reasoning. The entire stack — from tokenizer and encoder to the generation model — is developed in-house, with no dependency on external APIs or third-party hosted models. This ensures that model weights, embeddings, and user queries remain fully under the control of the deploying organization, enabling adherence to internal policies and regulatory frameworks.
DAKSH is inherently multilingual, supporting Indian and global languages with semantic fidelity. It recognizes language context automatically, adapts its internal processing accordingly, and delivers output in the user's preferred language — making it accessible and inclusive.
Finally, DAKSH emphasizes structured output enforcement. Responses can be returned in machine-readable formats such as JSON, XML, or Markdown, ensuring seamless integration into downstream systems like dashboards, workflows, or analytics pipelines.
Collectively, these design principles are realized through a flexible encoder-decoder model enhanced with specialized routing, attention modulation, and schema-aware generation modules — making DAKSH a scalable, secure, and future-ready enterprise AI assistant.