Dakshini℠ is built on a modular, scalable, and multilingual architecture designed to deliver natural Odia conversations while supporting over 75 languages. The system combines DeepQuery’s knowledge engine, retrieval augmentation, language fine-tuning, and proprietary components to ensure accuracy, consistency, and real-time performance.
5.1 Core Architecture
At the center of Dakshini℠ is the DeepQuery Intelligence Engine, which manages:
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Language detection
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Query understanding
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Retrieval of factual information
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Response generation
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Conversation flow management
This modular approach allows organizations to plug the agent into different environments (WhatsApp, web, mobile apps, or APIs) without changing the underlying intelligence.
5.2 DeepQuery KB Engine
The DeepQuery KB Engine is responsible for grounding the AI in accurate information. It enables:
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Integration of documents, FAQs, and domain knowledge
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Version-controlled updates without retraining the model
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Real-time retrieval using RAG
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Fast indexing for large datasets
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Custom knowledge packs for different sectors (government, finance, education, health, etc.)
This ensures that answers remain consistent, factual, and aligned with organizational requirements.
5.3 Multilingual Language Layer (75+ Languages)
Dakshini℠ uses a multilingual model layer that can automatically:
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Detect the user’s language
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Switch responses between languages
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Handle mixed-language input (e.g., Odia + English)
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Maintain accuracy across Indian and international languages
This makes Dakshini℠ suitable for diverse user bases across India and global audiences.
5.4 Odia Language Fine-Tuning
To address Odia-specific challenges, the model includes:
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Custom Odia vocabulary expansion
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Script normalization for joint characters
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Phonetic mapping for natural pronunciation-based inputs
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Grammar alignment tuned with human linguistic review
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Error correction for informal spelling patterns
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Proprietary Odia adapters for better sentence flow
These enhancements allow Dakshini℠ to understand and generate Odia in a natural, human-like manner.
5.5 Retrieval-Augmented Generation (RAG)
RAG is used to keep responses factual and consistent. The pipeline works by:
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Receiving the user’s message
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Searching relevant documents/knowledge sources
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Ranking and selecting the best matching information
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Combining retrieved facts with the language model's generation
This ensures the AI does not hallucinate and always provides grounded answers.
5.6 Conversation Management Engine
Dakshini℠ includes a conversation manager that handles:
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Session context
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Follow-up questions
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Multi-turn conversations
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Clarification prompts
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Intent switches
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Memory of previous interactions (optional based on privacy settings)
This creates a smoother and more coherent conversational experience.
5.7 Performance & Scalability
The system is optimized for:
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Low-latency responses
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High concurrency (thousands of users at once)
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Cloud and on-prem deployment
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Lightweight API integration
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Secure handling of sensitive user interactions
Dakshini℠ can scale as organizations grow without degrading performance.