Unlike conventional static models that remain unchanged after deployment, DAKSH is architected for continuous evolution through dynamic fine-tuning and corpus enrichment. This capability allows the system to adapt to new enterprise knowledge, evolving domain requirements, and user behavior — ensuring long-term relevance and increasing response precision over time. To support this, DAKSH incorporates a flexible and secure corpus refresh mechanism that enables targeted updates without requiring a complete retraining cycle.
The first component of this mechanism is Delta Syncing. When new knowledgebase files are uploaded to the DAKSH platform — whether policies, SOPs, regulatory updates, or FAQs — the system automatically flags the changes, assigns version identifiers, and incrementally incorporates the updated content into its retrieval pipeline and fine-tuning memory. This eliminates the need for expensive full-model retraining while ensuring that the assistant reflects the latest institutional knowledge.
In addition, DAKSH integrates a Feedback-Driven Curation system. During production use, if users rate responses poorly, request clarifications, or encounter retrieval mismatches, these interactions are flagged for review. The system then generates synthetic contrastive examples — query-response pairs that emphasize correct intent mapping — which are used to fine-tune the model incrementally. This feedback loop helps the model improve its alignment with real-world expectations while avoiding overfitting.
To support deployment in multiple industries, DAKSH maintains domain-specific expansion slices. As the assistant is rolled out to verticals such as healthcare, education, logistics, or public administration, their respective corpora — including specialized terminology, compliance references, and user interaction patterns — are integrated into segmented knowledge zones. These zones are independently indexed and fine-tuned, allowing the model to respond accurately within the context of the active domain without contaminating responses across unrelated sectors.
This continuous adaptation framework ensures that DAKSH is not a static model but a living, learning AI layer — one that becomes more fluent, more accurate, and more personalized with each deployment cycle, thereby providing sustained value to both enterprise and public service environments.