Future Roadmap

DAKSH has established itself as a modular, multilingual, and context-aware AI assistant capable of powering knowledge access across government, enterprise, and citizen-facing domains. However, the evolving landscape of generative AI, multimodal systems, and enterprise requirements continues to shape DAKSH’s development trajectory. The roadmap ahead is focused on enhancing performance, accessibility, and capability — with deep attention to edge inference, real-time decisioning, active learning, and multimodal interaction.

This section outlines the key strategic directions and upcoming milestones that define the future evolution of DAKSH.

1. On-Device Inference and Offline Capabilities

To further extend reach into low-connectivity environments such as rural kiosks, government centers, and mobile field deployments, DAKSH is being optimized for on-device inference. This involves:

  • Quantizing models to INT8 or lower precision for edge compatibility

  • Reducing model footprint using distillation and sparsity techniques

  • Integrating local vector search engines for offline retrieval

  • Deploying STT and TTS locally using embedded frameworks (e.g., Coqui, Vosk)

Once complete, this will enable fully autonomous knowledge agents that function without internet access — serving regions where cloud dependence is not feasible, and ensuring data sovereignty for sensitive applications.

2. Multimodal Input and Output Integration

Current deployments of DAKSH are text and voice-driven. The roadmap introduces multimodal support, enabling the assistant to process and reason over:

  • PDFs, scanned documents, and images via OCR and Vision Transformers

  • Tabular data and spreadsheets using embedded table parsing models

  • Graphs and charts for describing and interpreting visual data

  • Video transcripts and audio snippets for search within media archives

This expansion will allow DAKSH to function in digital workplaces where content is not limited to plain text. For example, a user could upload a scanned utility bill or regulatory form and ask questions about it — with DAKSH parsing, interpreting, and generating a structured response.

3. Role-Based Access & Data Segmentation

To support more complex enterprise deployments, DAKSH will soon introduce fine-grained access control layers:

  • Role-based response shaping: e.g., auditors see compliance detail, users see summaries

  • Data segmentation by department, team, or region

  • Token-based authorization across APIs and SDKs

  • Session-specific knowledge overlays (e.g., finance data vs. HR knowledge)

This ensures context-sensitive output generation while maintaining strict access boundaries — critical for large organizations with diverse information hierarchies.

4. AI Feedback Loop and Continuous Learning

While DAKSH does not retrain on live user data by default (to ensure privacy and stability), future updates will include a controlled active learning pipeline that supports:

  • Flagged query sampling for retraining with human review

  • User-feedback-based fine-tuning (thumbs up/down, clarification requests)

  • Synthetic contrastive pair generation for underperforming categories

  • Versioned micro-updates to retrieval and generation logic

These mechanisms allow incremental, safe model improvement without introducing instability — helping DAKSH evolve continuously in response to user expectations and changing knowledgebases.

5. Native Dashboard and Analytics Modules

In response to enterprise demand, a comprehensive DAKSH Admin Dashboard is being developed to offer:

  • Real-time analytics: Query heatmaps, usage spikes, language distribution

  • Embedding monitoring: KB update sync, retrieval error tracking

  • Alerting tools: Latency alerts, misuse detection, high-risk PII exposure

  • Visual model comparison: A/B test performance of multiple LLM variants

This will empower IT teams and administrators with full control, transparency, and insight into how DAKSH performs in their environment.

6. Domain Expansion and Industry Templates

DAKSH will launch pre-configured templates for specific domains, including:

  • Healthcare (HIPAA-adapted diagnosis and service bots)

  • Education (Course advisor, academic record retriever)

  • Legal (Clause summarizer, citation resolver)

  • Compliance (Policy auditor, ISO/GDPR explainer)

  • Municipal services (SOP explainer, tax advisory, RTI assistant)

These templates will include domain prompts, UI configs, pretrained vector indexes, and voice support tuned to the industry — allowing 1-click onboarding for new verticals.

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