Client & Enterprise Challenge
Inaccurate demand forecasts led to $2.1M in annual stockout losses and bloated warehouse storage fees across 15,000 active retail SKUs.
The Technical Solution & Architecture
Rohit developed a hybrid time-series forecasting engine fusing sequential PyTorch LSTM trend embeddings with tabular XGBoost promotional features, predicting 90-day SKU re-order levels.
Verified Quantifiable Business Metrics
94.2% Forecast Precision
Improved SKU demand prediction accuracy from 68% up to 94.2%.
34% Lower Holding Costs
Reduced excess warehouse safety stock buffer requirements.
99.1% Stockout Prevention
Eliminated high-margin product stockouts during peak holiday shopping surges.
Executive Client Review
"Rohit's forecasting engine transformed our inventory management. We cut warehouse holding costs by 34% while virtually eliminating holiday stockouts."
— Executive Leadership Team, Multi-Channel Retail & E-Commerce Network
Want Similar Results for Your Organization?
Schedule a 1-on-1 technical scoping session directly with AI & Data Science Consultant Rohit.
Lead Architect
Rohit
AI & Data Science Consultant
2+ Decades AI ExperienceBuilding neural networks since 2004 at IIT Roorkee (mentored by Dr. Sunil Padhi, HOD Electrical Dept) and Unix CDR automation scripts at Xalted Bengaluru in 2007 (mentored by Srinivas Sir). Specializing in Agentic AI, Enterprise RAG, and MLOps.
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