PrithviX

Created by team Search Engine on November 23, 2025

PrithviX is an Adaptive AI-Driven Warehouse and Trend Forecasting System that transforms inventory management through automation, sustainability intelligence, and explainable analytics. Built on IBM Watsonx Orchestrate, it unifies social-trend detection, demand forecasting, supply-chain awareness, waste analysis, and risk simulation into one automated workflow. The process begins when Orchestrate activates PrithviX’s social-signal engine, which collects anonymized trend patterns from TikTok, Instagram, and YouTube. These signals are cleaned and validated to separate real demand from short-term hype. The refined signals, combined with historical sales logs, feed into the forecasting engine to produce short- and medium-term predictions. SHAP explainability reveals which factors influence each forecast, increasing clarity and trust. Orchestrate then connects these predictions to the supply-availability agent, which evaluates stock levels, shipment delays, expiry risks, and supplier reliability. This ensures insights match real operational constraints. PrithviX’s sustainability module analyzes carbon footprint, packaging impact, waste probability, and deadstock risk to support responsible decisions. A simulation engine models thousands of possible outcomes, estimating risks related to overstocking, lost revenue, waste generation, and environmental impact. All outputs flow into an XAI decision card presenting trends, supply feasibility, sustainability impact, and risk levels. Managers receive one clear recommendation: stock more, stock less, or wait. PrithviX stands out by combining trend intelligence, forecasting, supply logic, sustainability scoring, and risk modeling into a single orchestrated ecosystem. It supports SDG 9 by modernizing warehouse operations, SDG 12 by reducing waste, and SDG 17 by integrating with ERP systems like SAP and Oracle. The result is a smart, transparent, and sustainable warehouse intelligence platform.

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"Great work!! Demo worked and technically impressive multi-agent warehouse forecasting system with deep IBM ADK integration,"

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Banani Mohapatra

Senior Manager Data Science

"Brilliant idea! It effectively addresses the warehouse forecasting use case and has strong adoption potential in e-commerce. The integration of social media signals is excellent, and the demo is clear. Great work!"

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Bhavnish Walia

"More emphasis has to be made on identifying a clear problem and industry. Warehousing problems could be unique to different industries. As forecasting models could also take into considerations various signals including social media, more clarity is needed on the complexity of these agents, and whether these agents are mainly contributing with the external search of data that is fed into forecasting models for accurate predictions. "

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Hari Kanagala

Group Product Manager AI