Short answer: the full flow has four links — AI catches the enquiry on your LINE Official Account and identifies what's needed → key fields from the conversation (request type, budget, preferred time) are extracted automatically → a customer record is created in the CRM and an owner assigned → follow-up reminders are scheduled based on customer status. Technically this runs on the LINE Messaging API talking to your CRM's API. In practice, success doesn't hinge on the integration itself — it hinges on field design and on the mechanism for handing off to a human when the AI can't decide.
Why AI replies alone aren't enough
Here's where a lot of businesses are: the LINE Official Account has auto-replies (or an AI connected), so customer questions get answered — and then what?
- The conversation ends and the information stays inside LINE. It never becomes customer data.
- To follow up, sales has to go back and read the chat history.
- The customer asks again a week later and the AI doesn't remember where things left off.
- Who should follow up, and how far they've got, still lives in someone's head.
Replying is only the first link. An AI support agent that isn't connected to a CRM is a receptionist who doesn't write anything down.
The four links of a full integration
Link one: catching (LINE → AI)
The AI receives messages through the LINE Messaging API and does three things first: works out which kind of request this is (pricing / booking / after-sales), answers common questions using approved knowledge, and hands off to a human when it can't decide. The important part is that the answer scope has a boundary — the AI only answers what's been confirmed in the knowledge base, and pricing or unusual situations go to a person.
Link two: extracting (conversation → fields)
This is the link most auto-reply tools can't do, and the one with the most value: pulling structured fields out of the conversation — request type, budget range, preferred time, source. For fields that are missing, the AI can draft a follow-up question to fill them in.
Link three: recording (fields → CRM)
The extracted fields get written into the CRM: create (or merge into) a customer record, log the interaction history, assign an owner, mark the current stage. Merging matters — when the same customer comes back a second time, it has to attach to the existing record rather than opening a new one.
Link four: following up (CRM → reminders and drafts)
Once a record has a stage and an owner, the system prompts on a cadence: who's due for follow-up, with a draft message the AI has prepared, which the owner confirms before sending. This link is what turns "AI support" into a sales process.
Checklist of common breaks
| Break | Symptom | Missing link |
|---|---|---|
| Replies, no record | The conversation ends, data stays in LINE | Links two and three |
| Records, no merge | One customer, several records, duplicated data | The merge logic in link three |
| Records, no owner | The CRM has names, but nobody knows who's chasing whom | Owner assignment in link three |
| Names, no cadence | Follow-up happens when someone remembers; cold leads go unclaimed | Link four |
Adoption steps, in practical order
- Inventory the knowledge: organise common questions and standard answers into a knowledge base the AI can cite. This step is yours — nobody can do it for you.
- Define the fields: decide what the CRM needs to capture. Too many fields and the AI's follow-up questions get annoying; too few and sales picks up with no context. Start with the three to five things sales must know before opening their mouth.
- Set the handoff rules: what triggers a handoff (negotiation, complaints, low AI confidence), who it goes to, and how the conversation summary travels with it.
- Connect the APIs: LINE Messaging API to your CRM. If you have a CRM already, check whether its API supports writing and duplicate lookup. If you don't, starting with something lightweight is more practical than buying a large platform.
- Test and calibrate internally: simulate conversations with real scenarios, fix the responses, the fields and the handoff conditions, then go live.
When it fits, and when it doesn't
It fits service businesses where enquiries mainly arrive through LINE at a steady volume, and the team spends significant time repeating the same answers and copying information by hand — weddings, interior design, real estate and beauty are all typical.
It doesn't fit when enquiry volume is small (you can answer them yourself), or when the process isn't settled (there's no rule for the AI to learn). Also, adjust your expectations if you're hoping for "fully automatic closing" — the mature approach is AI on the front line, humans making the judgement calls, and important conversations always going to a person.
Common questions
Q: Do we have to replace our current CRM? Not necessarily. If your existing CRM has an API, evaluate connecting to it directly. The actual scope depends on the tool, its API and permissions — confirm it during the demo stage.
Q: Will customers know they're talking to an AI? We recommend saying so honestly. What makes the experience good isn't how human it seems — it's replying fast, answering accurately, and handing off smoothly when it should.
Q: What about data security? At minimum, require: data stored separately, encrypted, not used to train models for other clients, and exportable when the contract ends.
Q: Have you actually built this? Yes. Real estate: enquiries to the LINE Official Account get automatic listing replies, viewing appointments scheduled, and customer status written to the list (full case study). Weddings: enquiries are caught, organised into a customer record, then handed to a person (full case study).
Written by the PeakQi team. This flow is drawn from PeakQi's actual LINE Official Account integration projects (the real estate AI assistant, the wedding industry AI suite, and our group-buying platform). For the technical details of the LINE Messaging API, the official LINE documentation is the authority. Published 2026-09-02.