A good AI customer service example is more useful than a feature list, because it shows what a well-handled conversation actually looks like and why each reply works. This article walks through three conversations an AI agent on the levelpartner.ai platform could handle for an existing client: a status question, a document request and a frustrated client who needs a person. Each is followed by what made it work and the settings behind it, then the steps to build the same thing for your firm.
Example one: the status question
A client of a 40-person marketing agency messages on WhatsApp at 7pm.
Client: Any update on the new landing page? Was meant to be live this week.
Agent: Hi Priya. I can see the landing page is with your account manager, Tom, for final review, due back to you on Thursday. Would you like me to ask Tom to confirm by message tomorrow morning?
Client: Yes please.
Agent: Done. Tom will message you before 10am tomorrow.
Why it works: the agent answers from the client's record, where the project stage and owner are stored, rather than guessing. It does not promise the page will go live; it reports the stage and offers a specific action. Behind the scenes it creates a task for Tom and notes the promise on the contact.
Example two: the document request
A client of a mortgage brokerage texts on a Saturday.
Client: What do you still need from me for the application?
Agent: Hi James. We still need your last three months of payslips and a recent bank statement. You can upload them through the secure link we sent on Tuesday. Shall I send that link again?
Client: Yes, lost it.
Agent: Sent. Once both are uploaded, Sarah will review them on Monday.
Why it works: the outstanding documents are tracked as fields on the contact, so the agent reads a list rather than interpreting anything. It never comments on the application's chances. It resends an existing secure link instead of asking for documents by text.
Example three: the frustrated client
A client of a real estate agency uses web chat.
Client: This is the third time I have asked about my deposit return. Nobody is getting back to me.
Agent: I am sorry you have had to ask three times. I am passing this to the lettings manager now as urgent, with your previous messages, so you do not have to explain again. Someone will contact you today before 5pm.
Why it works: the agent recognises frustration and a money topic, both on its hand-off list, and does not try to resolve it. It acknowledges the problem in one sentence, escalates with context and gives a specific time. It does not apologise repeatedly or defend the firm.
Before you start
- Client-facing fields on your contact records that the agent can read: project stage, account manager, outstanding items.
- Workflows or team habits that keep those fields up to date.
- A hand-off list: topics and signals that always go to a person, such as money disputes and complaints.
- The channels your clients actually use, connected to your account.
How to set it up
- Go to AI Agents > Conversation AI and click + Create Bot.
- Select Guided Form Setup.
- Click Create New Bot.
- Choose the bot type: General Q&A or Appointment Booking.
- Select or create a Brand Voice.
- Configure Bot Settings.
- Configure Bot Goals.
- Save the bot.
- Choose Off, Suggestive or Auto-Pilot as the bot's mode.
What these examples have in common
Each reply is short, specific and ends with a clear next step. None of them invents information. Each relies on data that a person keeps current, which is the unglamorous part of AI customer service. If your project stages and outstanding items are not maintained, the agent has nothing true to say.
Suppose account managers answer around 150 status questions a month by message, many in the evening. With the agent answering from project fields, account managers would see only escalations and specific requests. Measuring it is simple: count the status questions that still reach a person each month, and ask the managers to log the hours they no longer spend on them.
The common mistake is building the agent before cleaning the data. An agent reading a stale project stage tells clients wrong things with confidence. Fix the fields and the habits that update them first.
When not to automate client service
Keep high-value, relationship-led conversations with people. A client who pays a large retainer and expects their account director to reply personally may see an AI answer as a downgrade. Use the agent for out-of-hours cover and simple lookups for those clients, not as the first reply in office hours.
Troubleshooting
The agent gives outdated statuses. The fields are not maintained. Add a workflow step that updates the stage when work moves on, or a weekly reminder to the owner.
It answers questions it should hand off. The hand-off list is vague. Name specific topics and phrases, such as refund, deposit, complaint and solicitor.
Clients ask for things the agent cannot see. Either add the data to the record or add the topic to the hand-off list.
Tasks pile up for one person. Route tasks to teams, not individuals, where several people can act.
Frequently asked questions
Is this AI customer service example realistic for a small firm?
Yes, provided the client data is in the platform and kept up to date. The agent is only as good as the fields it reads. Most firms start with one question type and add others.
Can the agent see confidential client information?
It can read what you give it access to through the contact record and knowledge base. Keep sensitive documents out of the knowledge base and use secure links for anything personal. Decide field by field what the agent may mention.
Where can I see more examples?
The conversational chatbot examples guide covers sales conversations, and artificial intelligence in CRM examples covers CRM uses. For the wider support set-up, read AI customer support and artificial intelligence customer care.