YundaDeskLINE is close to a national app in Japan, and it is a primary support channel in Taiwan too — yet a lot of cross-border teams still run LINE support the slow way: an agent typing every reply from scratch, honorific register slipping in Japanese, or a translated-sounding phrase in Traditional Chinese that instantly signals “this is not a local team.”
LINE is already connected into the YundaDesk omnichannel workspace, so those high-frequency replies don’t have to live only in an agent’s head. Turn them into knowledge base entries, and the AI customer service can pick them up automatically in the customer’s own language while agents handle the rest.
Why LINE support especially needs scripting
Japanese honorific speech leaves very little room for error — one wrong register can read as impolite even when the content is correct. Traditional Chinese is more forgiving on tone, but Taiwanese users are quick to notice phrasing that reads as a direct translation from Simplified Chinese conventions rather than something written by a local team.
Once high-frequency replies are written out in each language and stored in the knowledge base, the AI customer service follows the language the customer is writing in without any extra configuration. That’s the same principle we cover in what an omnichannel inbox actually does: more channels are not a burden as long as the scripts and knowledge base keep up.
Opening and identity confirmation
Structure openers around three moves: greet, ask for an order number or registered email, and set the expectation that a human agent is available if needed.
- Japanese register: a polite, formal opener that thanks the customer for reaching out, asks for the order number or email on file, and states that the team will check the current status first.
- Traditional Chinese register: a warm but not overly formal opener that thanks the customer, asks for the order number or registered email, and confirms a human agent is available for anything that needs escalation.
Keep both versions short. Long openers read as scripted in either language, and customers in Japan and Taiwan both tend to want the fastest path to an answer.
Shipping and delivery inquiries
Delivery-time sensitivity is high in both markets — Japanese customers in particular expect a clear, verifiable answer rather than a vague promise.
| Scenario | What the script should do |
|---|---|
| Shipping timeline | State that the order follows the timeline listed on the page, confirm tracking will follow once shipped, and ask for the order number to check current status. |
| Shipment delay follow-up | Acknowledge the wait, confirm the order is in the processing queue, and note that any delay will be escalated to the fulfillment team. |
| Tracking not updating | Explain that tracking can pause temporarily during customs clearance or carrier handoff, then ask for the order number to pull the latest record. |
Aftersales and de-escalation
In Japanese support, the expected order is apology first, explanation second, action third — skipping the apology reads as abrupt even when the explanation is accurate. Traditional Chinese customers care less about that sequencing and more about whether a concrete next step is offered.
- Japanese: open with a sincere apology for the inconvenience, confirm the details will be reviewed, and state a human agent will step in if needed.
- Traditional Chinese: acknowledge the inconvenience, confirm the order will be checked right away, and share what happens next.
Both versions should stay factual. Avoid words that edge toward a promise — “definitely,” “right away,” “guaranteed” — in either language, since those are the phrases that turn a de-escalation script into an accidental commitment.
Refunds and high-risk scenarios: acknowledge, don’t promise
This is the part of the library that needs the tightest editing. Polite register in both Japanese and Traditional Chinese can accidentally slide into something that sounds like a commitment — an honorific phrase that reads as “we will definitely handle this for you,” for example. None of that belongs in a template the AI can use on its own.
| Scenario | What the script should do |
|---|---|
| Refund request | Acknowledge the request, explain that refunds require order verification, and state it is being routed to a team member. |
| Compensation request | Apologize for the inconvenience, explain that compensation needs manual approval, and ask for the order number and details. |
| Threat of a bad review or complaint | Thank the customer for raising it directly, and state the case is being escalated immediately to avoid giving an inaccurate answer. |
Turning scripts into knowledge base entries
A shared document full of phrases doesn’t help the AI customer service — it needs entries broken out by scenario, with the trigger phrasing, the standard reply, whether the AI can answer directly, and when it must hand off. For a walkthrough of how to structure that, see how to build a knowledge base the AI can actually use.
- Tag each entry with its language (Japanese / Traditional Chinese)
- Mark whether the AI can answer directly or must escalate
- Add common phrasing variations customers actually use
- Flag high-risk entries with a “do not promise” list to catch overly polite phrasing that reads as a commitment
- Update entries promptly when a promotion or policy changes
Once scripts are in the knowledge base, the review question is not how many phrases were written. It is how much less time agents spend composing the first useful reply. Rehearse with historical conversations first, then watch the weekly median first response time.
Median human first response after LINE scripts enter the knowledge base (illustrative)
When an agent notices a script isn’t quite right during a live conversation, correcting the AI in the workspace generates a suggested update rather than changing anything on the spot — a manager reviews it before it becomes part of the knowledge base. That loop is covered in more detail in what “gets smarter with every use” actually means.
The risk in Japanese and Traditional Chinese support isn’t running out of things to say — it’s saying the wrong thing politely. Get the high-frequency scenarios into the knowledge base, let the AI handle the routine ones, and keep refunds, compensation, and complaints firmly on the human side. That’s how a channel as sensitive as LINE stays fast without losing its footing.