Dukaan Dost: AI Stock Forecast for Kiryana Stores

# Inspiration & Problem Neighborhood grocery (kiryana) shops in emerging markets operate on thin margins, high cash-flow constraints, and informal supplier delivery cadences. Most shopkeepers restock based on gut feel or basic static averages, resulting in frequent stockouts of high-velocity staples like milk, tea, and flour, or excess dead capital tied up in slow-moving inventory. # What Dukaan Dost Does Dukaan Dost turns daily sales logs into actionable inventory intelligence: - **Statistical Demand Forecasting:** Models seasonal weekday demand variations and level-smoothed trends across products over a rolling 7-day horizon. - **Dynamic Stockout Prediction:** Evaluates supplier visit cycles and delivery lead times to compute precise restock triggers, unit counts, and estimated restock costs. - **Rigorous Verification & Simulation:** Features a transparent 28-day historical replay simulator that proves stockout reduction versus traditional baseline ordering rules without data leakage. - **Accessible, Multilingual AI Ordering:** Uses AMD Token Factory open models to convert mathematical forecasts into human explanations and supplier WhatsApp restock messages in both English and Roman Urdu, backed by a strict anti-hallucination guardrail that blocks any draft containing unverified numbers. # Architecture & Tech Stack - **Frontend & Interface:** Lightweight, responsive vanilla web architecture with custom canvas-based data visualizations, printable distributor restock slips, and bilingual localization toggles. - **Forecasting Engine:** Deterministic level-smoothing and weekday factor algorithm executed client-side. - **AI & Acceleration:** Serverless API integration powered by AMD Token Factory running open open-weights LLMs for contextual explanation and verified supplier message generation.