VyapaarMate is developing a business-intelligence layer for Indian MSMEs that converts fragmented order, payment, customer, and booking data into actionable recommendations. The system combines rules-based automation, machine-learning-ready models, consent-aware customer segmentation, and MSME-specific workflow intelligence to help small businesses run direct commerce without depending only on marketplaces.
Architecture
System layers
Collects tenant-scoped orders, bookings, catalog items, customer history, payment status, WhatsApp consent, and business settings.
Runs low-cost rules, weighted moving averages, RFM scoring, payment priority formulas, and health-score calculations without mandatory paid AI calls.
Converts signals into plain actions such as prepare more stock, message opted-in customers, follow up payments, or promote top repeat items.
Keeps each business isolated by businessId and treats WhatsApp marketing consent separately from order-status communication.
Keeps the deterministic engine ML-ready so future prediction models or optional LLM summaries can be enabled behind feature flags.
PRISM-ready explanation
The innovation is not only in digitising orders but in creating an MSME-specific decision engine that converts local-business activity into simple owner actions. Unlike generic e-commerce or CRM tools, VyapaarMate is designed around Indian local commerce behaviour where discovery, repeat orders, reminders, and customer communication often happen through WhatsApp, while payments and records need structured tracking.
The first version uses deterministic calculations, data aggregation, and explainable templates. Optional LLM integration can be added later for summaries, but the product works without OpenAI, Gemini, Claude, or any paid AI provider.