Product architecture and development
Technology & Innovation
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
How local commerce data becomes an owner action
Customer Channel
Website / WhatsApp
Order & Booking Data
Payment Data
Customer History
VyapaarMate Intelligence Engine
Owner Dashboard
Recommended Business Action
System layers
Low-cost intelligence that fits MSME operations
MSME Data Layer
Collects tenant-scoped orders, bookings, catalog items, customer history, payment status, WhatsApp consent, and business settings.
Intelligence Engine
Runs low-cost rules, weighted moving averages, RFM scoring, payment priority formulas, and health-score calculations without mandatory paid AI calls.
Owner Action Layer
Converts signals into plain actions such as prepare more stock, message opted-in customers, follow up payments, or promote top repeat items.
Consent & Privacy Layer
Keeps each business isolated by businessId and treats WhatsApp marketing consent separately from order-status communication.
Future ML Extension Layer
Keeps the deterministic engine ML-ready so future prediction models or optional LLM summaries can be enabled behind feature flags.
Pilot validation
Decisions owners can explain and evaluate
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.
Practical MSME intelligence
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.