AI Chatbots for Customer Service
Repeated shipping, returns, and product questions can take time away from cases that need your judgment. Learn how to choose a chatbot that answers from company information, shows when it is unsure, and hands the conversation to your team when needed.
A practical buying guide
AI chatbots for customer service: what they are—and what they are not
AI chatbots for customer service are software systems that hold conversations with customers and help answer support questions. Some follow fixed menus or keywords; AI-powered ones can interpret varied wording and draft contextual responses. For a support team, the useful distinction is not simply “AI or no AI.” It is whether the bot can answer from dependable company information, recognize when that information is not enough, and give a person a useful handoff.
Common, repeated questions are often a sensible starting point: delivery estimates, published return rules, product specifications, opening hours, or where to find a help article. Customers can get an answer without waiting for a teammate, while people can focus on cases that need judgment or investigation. But an answer bot and an action-taking system are not the same thing. To look up an order or issue a refund, a system needs access to the relevant live data and connected actions. A page explaining your returns policy does not tell a bot whether a particular parcel has arrived or whether a particular customer qualifies for a refund. For a broader tool-selection checklist, see how to choose an AI chatbot for customer support.
How are AI chatbots used in customer service?
A content-grounded chatbot retrieves relevant passages from sources you provide, uses them to draft an answer, and checks whether those passages support the draft. When the evidence is sufficient, it can answer with citations. When it is not confident, a safer flow tells the visitor it is unsure and opens a ticket rather than inventing a policy.
A question about returning shoes that have been tried on illustrates why policy conditions matter. The bot should answer only when the current returns policy supports the response, and cite that source. If the policy covers unused items but says nothing about tried-on shoes, it should not infer an exception or promise a refund. The team can review the ticket, answer the shopper, and improve the approved knowledge if the answer should be reusable.
Keep the source material accurate and easy to interpret. Review unanswered questions and look for missing, conflicting, or outdated policy text. A chatbot cannot make an unclear returns page clear by guessing; the underlying content needs a human owner and a review loop.
How to choose a safe first scope for a customer service chatbot
Before choosing a tool, list the questions that repeat and mark which require live customer or order data, judgment, or an action. Ask how the tool handles an unsupported question, what information it collects before a person takes over, which customer channels are available, and which integrations actually work today. Check citations and test with real customer phrasing, including awkward edge cases. A narrow launch scope helps set clear expectations: answer questions supported by available sources and provide a human route for questions outside that scope. After launch, review unanswered questions to identify content gaps and improve the handoff.
Where momo fits in a customer support workflow
momo is a source-available AI support desk for teams that want answers grounded in their own content and a shared inbox for the conversations that need a person.
Answers use your own knowledge
It retrieves passages from your business content, drafts a response, and checks the draft against those sources before answering with citations when confident.
Visitors can see which company information supports a confident answer.
Uncertainty leads to a ticket
When it is not confident, it tells the visitor it is not sure and opens a ticket for your team instead of treating a missing policy as an answer.
Unsupported questions have a route to a human.
The inbox is part of every plan
Teammates can take over any conversation live, handoff collects the details your business chooses, and a human answer can be saved as approved knowledge.
The team can handle exceptions and improve reusable answers in the same support workflow.
Compare the plan fee with the work your team still handles
momo uses a flat EUR plan fee with an included number of AI conversations, not a per-resolution fee or credit packs; forecast recurring questions, handoffs, team access, channels, and knowledge needs together.
Included: A helpdesk inbox is included on every plan, including Free. · Paid plans allow up to 25 AI replies in an AI conversation; Free allows up to 10. · Yearly billing is 11 times the monthly price.
Free
Try momo with real visitors.
€0€0 forever
- 30 AI conversations, one-time
- 1 agent, 1 team member
- Website widget
- Helpdesk inbox
- Basic analytics
- 2 MB of knowledge, 10 sources
Starter
One agent, steady traffic.
€29€27per month
- 500 AI conversations / month
- 1 agent, 2 team members
- Website widget and email
- Helpdesk inbox and Help Center
- Basic analytics
- No momo badge
- 20 MB of knowledge, 50 sources
Growth
More agents, more channels.
€129€118per month
- 2,000 AI conversations / month
- 3 agents, 5 team members
- Slack, Shopify, WhatsApp, Instagram, Messenger
- API, Zapier and webhooks
- Advanced analytics
- 100 MB of knowledge, 200 sources
Scale
High volume, every channel.
€399€366per month
- 6,000 AI conversations / month
- 10 agents, 10 team members
- Every channel
- API, Zapier and webhooks
- Advanced analytics
- 500 MB of knowledge, 1,000 sources
Need more resources or custom solutions? Contact us for Enterprise plans
Customer service chatbot questions
Use these answers to decide what a bot should handle, what it needs access to, and where your team should take over.
Which AI chatbot is best for customer service at a small online store?
Choose based on the questions you need answered, the sources the bot can use, the channels your customers use, and the handoff your team can manage. For a store whose first need is published product and policy answers, look for source-backed responses and a clear route to a human. If your priority is order lookup or automatic return processing, verify that the tool has the required live data access and actions; policy content alone does not provide them.
Can a customer service chatbot answer order-status questions without access to order data?
It can explain general shipping or delivery policies from company content, but that does not reveal the status of a particular order. A specific order-status answer requires access to the relevant order information. Without that access, route the customer to a human or to the business’s existing order-status process rather than presenting a policy as a live update.
What should a customer service chatbot do when it cannot support an answer?
It should make the uncertainty clear and provide a route to a person, rather than filling a gap with a plausible-sounding answer. A useful handoff collects the details the business needs and gives the teammate the conversation context. The team can then answer, and where appropriate, approve that response as reusable knowledge.
How do I estimate chatbot costs from conversation volume and team handoffs?
Start with the recurring questions you expect the bot to handle, then compare that expected usage with each plan’s included AI conversations. Also account for who needs access, which channels and knowledge limits matter, and how much human work remains in tickets and exceptions. With momo, the plan fee is flat, there is no per-resolution fee or credit pack, and the Free allowance is one-time rather than monthly.
Can customer service chatbots process returns or refunds automatically?
Only if the chatbot is connected to the systems and data needed to carry out those actions, with suitable rules and permissions. A bot that can explain a published returns policy has not thereby processed a return or approved a refund. momo answers from business knowledge and hands unsupported questions to the inbox; Shopify is in preview, and its documented scope does not establish order lookup or return and refund actions.
How should I launch a customer service chatbot without frustrating customers?
Begin with a narrow set of questions that have clear, maintained answers in your content. Test ordinary phrasing, edge cases, and questions the content cannot answer; check citations and the uncertainty path before making the chatbot available to customers. Then review tickets and unanswered questions so the team can fix source gaps and keep human judgment in the cases that need it.
Start with the questions your customers already ask
Try a source-grounded answer and human handoff with the support questions your team knows best.