CatOrder: WhatsApp orders to draft orders

Food and beverage distributors still take orders the way their customers talk: a WhatsApp at eleven at night, a voice note from the car, a photo of a handwritten list. Someone at the order desk retypes each one, guessing what "the usual" means and whether the price is the one agreed. Nobody notices the bar that has gone quiet until it has moved to a competitor. CatOrder does the reading. Gemma 4 31B, served on AMD Instinct GPUs through AMD Token Factory, extracts every line from text, photos and voice-note transcripts. Code then matches each line to the catalogue, resolves "the usual" from the order history and checks the agreed terms, flagging wrong prices, odd quantities and ambiguous products. Every line points back to the exact words it came from. A person confirms, and Gemma drafts the WhatsApp reply. CatOrder also looks ahead. catorder-nbr, a small PyTorch model trained with ROCm on an AMD Radeon Pro W7900D, predicts each customer's next order date and basket: who has gone quiet, what they forgot, what to buy next week. Results, on synthetic data: on 237 WhatsApp and email messages never used to tune the prompt, 232 orders entirely right (98%) and 99.7% of lines right. Every miss was reviewed by hand (217 of 237 before that review); the rules are in the repo. On 135 held-out customers the forecast beats the strongest simple baseline on basket F1 (0.823 vs 0.803), ties it on the date and trails it on churn. Live WhatsApp through Twilio; TypeScript, Hono and SQLite; MIT licensed. Next: connectors to the back-office systems small distributors already use, now cheap to build with AI-assisted coding. Built by Emanuele Menon.