DAKSH begins its intelligent response pipeline with a robust and structured document ingestion framework, purpose-built to handle a wide range of enterprise knowledge formats. The platform accepts user-uploaded documents and knowledge sources in multiple formats — including PDFs, DOCX, HTML pages, Excel spreadsheets, and JSON files — along with annotated transcripts, structured manuals, and policy documents. This multi-format compatibility ensures that organizations can onboard their existing content without additional preprocessing overhead.
Once a document is uploaded, DAKSH initiates a multi-stage content transformation process to convert raw material into search-optimized knowledge units. The first step is semantic chunking, where the system breaks the content into logically coherent segments. Unlike naive paragraph-based segmentation, DAKSH applies context-aware rules that identify and preserve semantic boundaries — such as headings, sub-sections, enumerated lists, and table rows — ensuring that each chunk represents a meaningful unit of information.
Following chunking, each segment is enriched with contextual metadata, including the originating document name, section title, paragraph index, upload timestamp, content language, and access permissions. This metadata not only facilitates retrieval filtering (e.g., by department, version, or language) but also improves traceability during output generation, allowing the assistant to cite source references or restrict access based on user roles.
The enriched chunks are then passed through DAKSH’s embedding generation engine. Each chunk is transformed into a high-dimensional vector using a proprietary embedding model, which is fine-tuned to preserve domain-specific semantics such as legal phrases, technical nomenclature, or financial constructs. Unlike generic embeddings that capture only surface-level meaning, DAKSH’s embeddings encode contextual depth and structural intent — enabling more accurate and trustworthy retrieval.
These vectors are then indexed in a high-performance vector database capable of approximate nearest neighbor (ANN) search. This allows DAKSH to perform ultra-fast, semantically relevant retrievals across tens of thousands or even millions of document chunks — forming the foundational memory layer that powers DAKSH’s Retrieval-Augmented Generation (RAG) architecture.