What you are probably dealing with
A separate window per channel
LINE Official Account, Instagram, the Facebook page, the website form into an inbox. Agents switch between four interfaces, and a missed message is only a matter of time.
The same question answered a hundred times
Opening hours, shipping cost, the returns policy — most of the volume is the same handful of questions, all of it consuming agent time while genuinely complex cases wait in line.
Handover happens verbally
What a customer asked last week and what someone promised them lives inside individual chat windows. Change shift or change agent and the customer explains it all again.
The knowledge base ends up somewhere else
Teams want AI support but hesitate to push product data and pricing logic into an external service for training. Security review stalls the project.
Before and after
Before
- 1Four platform dashboards checked in rotation
- 2Common questions answered one by one by people
- 3Customer history scattered across chat windows
- 4Cross-team issues escalated by pinging someone in a group chat
- 5Service quality depends on individual memory
With AI GO
- 1Every channel flows into one contact centre
- 2AI answers common questions from your knowledge base
- 3Customer history is consolidated and visible on handover
- 4Cross-team work becomes a tracked ticket
- 5Response content and time are measurable
Modules involved
Start with a single channel if you prefer — once it runs smoothly, bring the rest and the ticket workflow in.
多渠道智能客服
全渠道客服整合模板。統一管理 LINE OA、Instagram、Facebook 粉專等多來源訊息，搭配 AI 智能客服自動回覆。AI 使用租戶自備的 OpenAI 金鑰（不消耗平台 AI 額度），知識庫存放於租戶自己的 OpenAI 帳號。
MessagingLINE 客服管理
LINE OA 即時客服整合模板。自動接收 LINE 訊息至聯繫中心，並可從系統內回覆客戶；聯繫中心顯示官方帳號真實名稱。AI 自動回覆使用租戶自備的 OpenAI 金鑰（不消耗平台 AI 額度），知識庫存放於租戶自己的 OpenAI 帳號。
Messaging客服工單中心
獨立客服工單系統：多管道工單受理、回覆串與結案滿意度、知識庫與常見問題管理、派工規則自動指派，內建 AI 建議回覆（比對 FAQ 與知識庫組 context，敏感議題自動轉人工）。
CRMIT 服務台
企業 IT 服務台：需求申請與派工、問題追蹤、軟硬體資產管理、機房巡檢與廠商進出管制。內建派工狀態流轉、問題單狀態機、授權超用與保固到期警示。
OperationsPlatform capabilities
Every channel, one inbox
LINE Official Account, Instagram and Facebook pages feed into one contact centre, which shows the real official-account name so you always know which account a customer came from.
The knowledge base stays in your account
AI replies run on your own OpenAI key. The knowledge base sits in your OpenAI account, consumes no platform AI quota, and your data does not land with a third party.
AI first, people for the hard ones
Routine questions are answered from your knowledge base. Anything the AI cannot handle is passed to a person along with the conversation so far.
Enquiries become tickets, not lost threads
Anything needing another team becomes a ticket with an owner, a status and a deadline — no more shouting into a group chat.
Labour cost you can recover
For a three-person support team at an average loaded rate of NT$280/hour, here is the monthly labour cost you can recover:
Answering repeat questions
Roughly 60% of original volume
- Hours / month
- 72 hrs
- Hourly rate
- NT$ 280
- Recovered / month
- NT$ 20,160
Switching between channels and searching
- Hours / month
- 30 hrs
- Hourly rate
- NT$ 280
- Recovered / month
- NT$ 8,400
Handover and history reconstruction
- Hours / month
- 18 hrs
- Hourly rate
- NT$ 280
- Recovered / month
- NT$ 5,040
Cross-team coordination and chasing
- Hours / month
- 20 hrs
- Hourly rate
- NT$ 350
- Recovered / month
- NT$ 7,000
| Work replaced | Hours / month | Hourly rate | Recovered / month |
|---|---|---|---|
| Answering repeat questionsRoughly 60% of original volume | 72 hrs | NT$ 280 | NT$ 20,160 |
| Switching between channels and searching | 30 hrs | NT$ 280 | NT$ 8,400 |
| Handover and history reconstruction | 18 hrs | NT$ 280 | NT$ 5,040 |
| Cross-team coordination and chasing | 20 hrs | NT$ 350 | NT$ 7,000 |
| Total | NT$ 40,600 | ||
These figures assume a typical staffing profile. Real numbers depend on your team size and process — we rerun the model with your own data during a consult.
FAQ
What if the AI gets it wrong?
Will this run up a large AI bill?
Could customer conversations leak?
Can we start with LINE only?
Further reading
台灣中小企業找 FDE 實戰指南：30 天 AI 落地不是夢
你聽說了 FDE 前線部署工程師，但不知道怎麼找、費用多少、能做什麼？本文用台灣真實案例說明，FDE 如何幫中小企業在 30 天內完成 AI 導入。
企業 AI 導入FDE、傳統顧問、IT 部門：台灣企業 AI 導入該選哪個？
台灣企業導入 AI 有三條路：找傳統管理顧問、靠自己的 IT 部門、或引入 FDE 前線部署工程師。三者差異是什麼？哪種適合你的公司？
企業 AI 導入FDE 是什麼？前線部署工程師完整指南（2026 台灣版）
FDE（Forward Deployed Engineer，前線部署工程師）是目前台灣最熱門的 AI 職缺，比 AI 科學家還搶手。本文從零解釋 FDE 是什麼、做什麼、為什麼台灣企業現在最需要他。
Other solutions
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