2026 · Demand-forecasting + inventory copilot
KiranaIQ
A demand-forecasting and inventory copilot for kirana stores: snap a bill, get per-SKU LightGBM forecasts, SHAP explanations, reorder quantities, and price experiments, over a Telegram bot.
Why I built this
Small kirana stores run on memory and gut feel, with no analytics layer between a paper bill and a reorder decision. I wanted to see how far disciplined classical ML could go on that problem honestly: forecast demand per SKU, explain every number in language a shopkeeper would trust, and prove it against real baselines on synthetic data before ever claiming it works in a real shop.
Current status
Primary interface is a Telegram bot (built, token-gated, 17 handler tests). As of June 2026, Telegram is blocked in India under a Government of India order, so the live bot is not reachable from India without a VPN; it is deployable to a cloud host, with a webhook/alternative channel as the fallback.
Architecture
Five capabilities in one copilot
- OCR ingestion · reads a photo of a bill or GST invoice into SKU-level line items with reconciliation checks
- Forecasting · global LightGBM (Tweedie) per-SKU demand vs seasonal-naive and AutoETS
- Explainability · SHAP turns each forecast into plain language (festivals, paydays, day-of-week)
- Reorder + cross-sell · newsvendor quantities and market-basket recommendations
- Experiments · A/B harness with Bayesian and sequential tests
Tech stack
Technologies used
core
infra
tools
Key highlights
Proof points
- 01
Per-SKU demand forecasting with a global LightGBM model (Tweedie) against seasonal-naive and AutoETS baselines: measured WAPE 35.8 percent versus 62.1 percent for seasonal-naive on synthetic retail data.
- 02
SHAP turns every forecast into plain language a shopkeeper understands (festivals, paydays, day-of-week effects).
- 03
OCR ingestion reads a photo of a bill or GST invoice into structured SKU-level line items, with reconciliation checks.
- 04
Newsvendor reorder quantities plus market-basket cross-sell, and an A/B harness (Bayesian and sequential tests) for price and promo experiments.
- 05
Telegram-bot interface (built, token-gated, 17 handler tests); 64 tests passing overall.
Focus areas
Explore the work