Skip to main content
AboutProblemsGuidelinesTerms & conditions

Ajrasakha AI-Assisted Expert Review & Moderation System

Problem Statement​

Agricultural advisory platforms must provide farmers with accurate, region-specific, and expert-validated guidance. However, existing AI-based systems often generate generic or unreliable answers, while human-only advisory models face scalability, delay, and quality inconsistency issues. There is currently no structured review, validation, and accountability framework that ensures every response shared with farmers is accurate, traceable, and continuously improved. Incorrect or unverified advice can directly impact crop health, yield, and farmer trust.

Objective​

To build a quality-driven agricultural advisory system that combines AI-assisted drafting with multi-level expert review and moderation, ensuring that only verified, region-aware, and accountable answers are delivered to farmers.

Work Done So Far​

  • Designed a multi-level expert review workflow for farmer queries.

  • Implemented AI-assisted draft generation to support experts (AI as co-pilot).

  • Built a Smart Allocation Engine based on region, crop, and availability.

  • Enabled three-level expert validation before publishing.

  • Integrated moderator-controlled final approval.

  • Created a Golden Database (GDB) of verified agricultural answers.

  • Implemented expert performance, reputation, and incentive tracking.

  • Added turnaround time and quality monitoring mechanisms.

Key Features​

  • AI-assisted answer drafting with full human control.

  • Region- and crop-aware expert assignment.

  • Mandatory multi-level expert validation.

  • Moderator oversight and governance.

  • Golden Database of trusted, reusable answers.

  • Expert reputation, incentive, and penalty scoring.

  • End-to-end traceability and accountability.

System Flow​

Farmer Question → Smart Allocation Engine → Expert Author (AI-assisted) → Expert Review 2 → Expert Review 3 → Moderator Approval → Golden Database → Published Answer to Farmer

Technologies Used​

  • Backend & Logic: Python-based workflow orchestration

  • AI / NLP: Large Language Models for draft assistance

  • Data Layer: Structured databases for Q&A and expert actions

  • Dashboards: Expert and Moderator interfaces

  • Analytics: Performance and turnaround monitoring

Future Scopes​

  • Crop-specific AI models and local language support.

  • ML-based detection of low-quality or risky answers.

  • AI-driven expert routing and load balancing.

  • Farmer feedback integration into expert reputation.

  • Compliance checks and explainable moderation decisions.

  • Predictive alerts for crop risks and regional issues.

Impact​

Ajrasakha ensures that farmers receive accurate, trustworthy, and region-relevant agricultural advice while maintaining scalability through AI assistance and reliability through structured human validation. The system improves answer quality, expert accountability, and farmer confidence at scale.