DAKSH (DeepQuery Autonomous Knowledge and Support Handler) is an enterprise-grade AI assistant framework built to deliver secure, scalable, and context-aware support across structured knowledgebases. Designed to operate in multilingual environments, DAKSH combines retrieval-augmented generation (RAG), modular APIs, and serverless deployment to create a seamless user interaction layer over enterprise data.
Unlike traditional chatbot systems or general-purpose LLM deployments, DAKSH is engineered with fine-tuned retrieval pipelines, robust access control, and native support for both text and voice interfaces. The system is capable of operating across domains, including governance, customer service, internal knowledge management, and data compliance monitoring.
DAKSH emphasizes modularity, cost-efficiency, and performance under constrained environments, enabling deployment in cloud-native, hybrid, or edge-based infrastructures. Its serverless backbone ensures auto-scaling capabilities, while its language model interaction layer is tightly coupled with context-driven data embedding and chunked retrieval strategies.
This whitepaper outlines the technical design, architectural components, data handling flows, and future roadmap of DAKSH.