mall-ai-after-sales-platform
An AI-powered e-commerce after-sales platform combining RAG, multi-step agent workflows, and human handoff.
Combine LLM agent orchestration with authoritative Java backend validation and human handoff.
Mall AI After-Sales Platform is an open-source platform designed for online stores to handle customer support and post-purchase service. It pairs Python FastAPI agent orchestration with a Java Spring Boot backend to enforce transaction safety and policy checks. You should use it if you need AI support workflows with explicit customer confirmation steps and human agent escalation.
What it is
The system processes customer requests, answers policy questions using RAG, and manages multi-step return or refund workflows. It generates action proposals that require explicit customer confirmation before committing updates to the primary Java backend. When cases need manual intervention, it hands off full context to human support representatives without losing conversation history.
Who it's for
This platform suits software engineers and technical e-commerce teams who want to build controlled AI support pipelines. It is not suitable for non-technical merchants looking for a one-click SaaS plugin, as it requires managing microservices, databases, and message queues.
Key features
Unified Chat Routing
Routes customer messages automatically between simple Q&A, multi-turn task processing, and human handoff within one chat interface.
Controlled Action Proposals
Presents structured after-sales solutions to customers for review and approval before executing changes in your primary Java database.
RAG & Policy Verification
Combines dense and hybrid search over versioned knowledge bases with live Java API checks for order eligibility and logistics status.
Human Support Workbench
Transfers complex cases to human staff, freezes automated AI writes, and uses local embeddings to group similar unresolved customer issues.
Up and running in 3 steps
Our honest verdict
What's great
- Authoritative Java backend re-validates identity and eligibility to prevent duplicate or invalid transactional writes.
- RAG engine enforces source evidence checking before returning policy answers to customers.
- Integrated human handoff automatically pauses AI actions to avoid race conditions during manual support.
- Includes offline semantic analysis using local vector search to group common unaddressed customer problems.
Where it falls short
- Complex infrastructure requires running FastAPI, Spring Boot, MySQL, Redis, MongoDB, and RabbitMQ.
- Requires managing and funding external LLM API access, such as DeepSeek keys.
- No pre-built mobile applications or turn-key cloud host options provided out of the box.
Pricing & license
The codebase is free under its open-source license. Your only expenses are hosting the containerized infrastructure and paying for external LLM API calls.
Open remote AI-training roles
867 open remote AI-training roles, updated November 14, 2026; most pay $20–$170 an hour. Zekai lists them free; the application goes to the employer’s platform.
Alternatives
These reviewed AI tools do the same job instantly — from our scored directory.
Use it, or have us set it up
FAQ
How does the tool ensure the AI does not modify database records directly?
The Python AI service only creates proposed actions. The authoritative Java Spring Boot service re-verifies user permissions, state, and business logic before executing any final write.
What happens when a customer requests to talk to a human?
The system creates a handoff ticket, presents queue status to the customer, and freezes the AI agent from performing automated database writes on that ticket.
