DAKSH was developed through a from-scratch training process, designed to equip the model with deep understanding of enterprise content structures, domain-specific semantics, and contextual reasoning requirements. Unlike general-purpose models that rely on unfiltered internet-scale corpora, DAKSH’s training data was meticulously curated from reliable, structured, and high-utility knowledge sources. These include technical manuals, policy frameworks, regulatory guidelines, enterprise process documents, annotated QA logs, and synthetically generated dialogues reflecting real-world usage scenarios.
The training strategy follows a phased curriculum, developed to progressively refine the model’s capabilities while maintaining strict alignment with enterprise knowledge workflows. In the initial phase — general language modeling — DAKSH was exposed to a broad yet domain-refined corpus across multiple industries. This phase built the foundational linguistic capabilities of the model, enabling it to understand sentence structure, vocabulary, and core grammar in both English and Indian languages.
Next, the model underwent structured fine-tuning, where it was taught to generate outputs that conform to predefined schemas and enterprise formats. These formats include key-value structures, regulatory checklists, tabular summaries, and hierarchical response trees — essential for generating automation-friendly outputs in business and governance environments.
Following this, DAKSH was subjected to contextual supervision using retrieval-augmented training examples. In this stage, the model learned how to synthesize coherent, context-aware answers by combining user queries with external memory inputs, such as document snippets or indexed policies. This significantly improved its ability to ground responses in real, relevant knowledge rather than relying solely on internal prediction.
Finally, the model was refined through behavioral tuning, using structured reward signals, heuristic rules, and enterprise-driven evaluation loops. This phase helped enforce tone consistency, structure adherence, and domain-specific response patterns while avoiding over-generation or hallucination.
The result of this training pipeline is a model that produces semantically accurate, logically consistent, and structurally validated responses, making it ideally suited for deployment in environments where precision, context alignment, and format compliance are non-negotiable.