Overview
M.A.R.K.E.T v4.0.5 is an enterprise-grade, self-hosted commerce automation platform that combines multi-channel customer communication, Google Maps competitor intelligence, role-based access control (RBAC), customer relationship management (CRM) composite identity persistence, automated request queue scheduling, and local/cloud large language model orchestration into a unified, zero-dependency Python service. It is built on FastAPI and SQLite, and includes a pre-compiled single-page application served directly by the backend. The platform provides a visual cards studio and workflow canvas for designing automation flows, along with an autonomous code synthesis feature powered by Qwen 2.5 Coder. It also offers a customer support handover center, a time and delay task scheduler, and a Meta integration gateway for social media interactions. The system emphasizes privacy and self-hosting, with no per-message fees and full on-premise deployment.
Pricing
Free
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Use Cases
- Teams use M.A.R.K.E.T to scrape Google Maps competitor data for business profiles, ratings, and reviews.
- Teams use M.A.R.K.E.T to generate strategic opportunity and gap analyses by combining market intelligence with their store catalog.
- Teams use M.A.R.K.E.T to track month-over-month growth indicators such as ratings and review velocity changes.
- Teams use M.A.R.K.E.T to manage customer support handovers, allowing human agents to claim conversations and suspend AI replies.
- Teams use M.A.R.K.E.T to maintain persistent customer profiles with composite UIDs and automated tier classification.
- Teams use M.A.R.K.E.T to schedule long-term actions like promotional voucher dispatches and follow-up notifications.
- Teams use M.A.R.K.E.T to integrate with Meta platforms for post publishing and real-time comment ingestion.
- Teams use M.A.R.K.E.T to design automation flows visually and synthesize executable Python modules from business requirements.
Features
Build visual card based workflows
Configure autonomous AI agents
Automate e-commerce workflows
Run with local language models
Use Qwen 2.5 or Ollama
Automate tasks through WhatsApp
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