Inquiries & customer service
Sort inquiries and draft multilingual replies, with human approval for sensitive terms.
Chenfeng AI builds custom agents, workflows and integrations for foreign trade and cross-border e-commerce teams. Inquiry handling, quotations, after-sales and product data, shaped around your rules.
Generic tools ask you to adapt. We start with the work that slows your team down, then define where AI can safely take over.
Sort inquiries and draft multilingual replies, with human approval for sensitive terms.
Generate materials from your pricing logic and reduce repeated entry and avoidable errors.
Turn manuals, policies and past answers into a bounded business knowledge base.
Connect the tools you already use and turn recurring actions into an executable workflow.
Pricing, delivery promises and claims stay behind a human review step by default. We define the data, permissions and boundaries before deciding what to automate.
Illustration only. This is not live operational data.
Explore a trade workspace, an Amazon operations proposal and a wellbeing support demo. Each overview makes its stage and scope clear.
Connect messages, opportunities, customer records and quotations in one flow, starting with how information moves.
Explore the projectDefine integrations, exception alerts and human approvals across orders, inventory, refunds, advertising and support.
Explore the projectExplore how supportive conversations, self-reflection, relaxation exercises and an operations workspace fit together.
Explore the projectShort, reviewable notes from the work behind a useful automation.
Quote automation software is solid for drafting quotes, but not for sending them on its own. This article covers what automation can take over, the three checks a human must keep, and a safe rollout order.
A practical AI workflow brief for foreign trade and cross-border e-commerce teams, covering the process, materials, permissions and human approval points to confirm before choosing tools.
A factual overview of the Chenfeng AI brand, its current operating entity, the teams it serves and the contact paths for discussing custom AI work.
Find the step that costs the most time or creates the most avoidable errors, then decide whether to expand.
Identify the stuck step, existing tools and available materials.
Set a staged plan around scope, integrations, data and permissions.
Every stage has a visible result and a checkpoint for adjustment.
Receive operating notes and agree on support and future iterations.