AI agent for customer support
AI agent for customer supportwith verified answers.
The AI customer support agent is an automated assistant that handles recurring questions, answers only from information you have approved, checks the status of an order or delivery, and hands over to a colleague, with a summary, any case that falls outside the agreed rules.
Your support team answers the same questions while the hard cases wait
A large share of requests repeat themselves: where is my order, how do I return something, when will the invoice arrive. Each one means opening two or three applications and writing a reply by hand. Meanwhile, customers with real problems queue up with everyone else, and answers vary from one colleague to another.
- Repetitive questions take up time that should go to complex cases
- To check an order's status, colleagues search manually in the store, the ERP and the courier's platform
- Answers are inconsistent, because the official information is not in one place
- Requests received out of hours pile up and are picked up late
Who it is for
- Online stores that receive the same questions about delivery, returns and order status every day
- Service companies with subscriptions or contracts, where customers often ask about invoices, deadlines and procedures
- Small support teams that want to reply faster without leaving unhappy customers unattended
- Companies that already have a ticketing system and want to cut the time spent on simple requests
What the AI customer support agent does
- 01
Answers from approved information
It uses the delivery, returns and warranty policies you have validated, not the model's general knowledge.
- 02
Checks an order's status
With the order number and an identity check, it queries the store or the courier and reports the actual status.
- 03
Collects complaint details
It asks for the order number, a description of the problem and the necessary photos, so the colleague receives a complete case.
- 04
Escalates to a colleague
It transfers the conversation with a summary when the customer asks for a person, is unhappy or the case falls outside the rules.
- 05
Creates or completes tickets
It opens a ticket in the support system, with the category, a proposed priority and the conversation history.
How it works
How a request is handled
The steps marked in yellow are approved by a person.
- Input
- AI
- Action
- Human in the loop
- Result
- 01
Input
Customer message
The question comes from the website chat, email or another connected channel.
- 02
AI
Understands the request
The agent identifies the topic and what information is missing.
- 03
Action
Searches and checks
It consults the approved policies or the order status in the connected systems.
- 04
AI
Writes the reply
It answers only if it has a source; otherwise it prepares the escalation.
- 05
Human in the loop
Colleague takes the exceptions
Sensitive cases reach the team, with a summary and history.
- 06
Result
Ticket closed
The conversation and its resolution stay recorded in the support system.
What information and systems the support agent uses
- Delivery, returns, warranty and payment policies, in their approved and current version
- Frequently asked questions and model answers validated by the support team
- Read access to orders in the online store or ERP, after the customer has been verified
- Delivery status through the APIs of the couriers you use
- The ticketing system, for creating tickets and transferring to colleagues
Possible integrations
- WooCommerce
- Shopify
- Zendesk
- Freshdesk
- Gorgias
- Intercom
- Fan Courier, Sameday or Cargus (API)
- WhatsApp Business
- The company ERP or inventory management system
- WooCommerce
- Shopify
- Zendesk
- Freshdesk
- Gorgias
- Intercom
- Fan Courier, Sameday or Cargus (API)
- WhatsApp Business
- The company ERP or inventory management system
Each integration's compatibility is checked during the analysis phase, for the systems and versions you use.
Control and transparency
What stays in human hands
- Approving refunds, compensation and any exception to the returns policy
- Formal complaints, tense situations and requests with legal implications
- Updating the policies and model answers the agent uses
- Regularly reviewing a sample of conversations for quality and tone
Limits and how we manage them
- It works best when the approved policies and answers are kept up to date, which is why we agree from the start who maintains them
- It can misread an ambiguous or hastily written message, which is why it asks for clarification or escalates
- Checking orders requires an API or stable read access to the store, ERP or courier, which we confirm during the analysis
- It does not make commercial decisions: it does not grant discounts, approve returns outside the rules or promise deadlines
How we measure the support agent's quality
We agree the indicators together before launch and track them afterwards. The values depend on the process, the data and the volume.
- Accuracy of answers
- The team checks a test set of real questions and a monthly sample of conversations.
- Escalation rate and its reasons
- We analyse which cases reach colleagues and whether they were escalated at the right moment.
- Quality of the handover to a person
- Colleagues note whether the summary they received was complete or whether they had to ask the customer again.
- Time to first response
- We compare response times in the ticketing system before and during the pilot.
- Customer feedback
- We track the ratings left at the end of the conversation and any negative comments.
How we implement the support agent, step by step
- 01
Request analysis
We group the requests from recent months by type and choose the ones suited to the agent.
- 02
Answer sources and escalation rules
We gather the approved policies, connect the order systems and decide when to transfer to a person.
- 03
Internal testing and pilot
The team tests the agent on real questions, then we activate it on a single channel, with close monitoring.
- 04
Launch and continuous improvement
We gradually add channels and update the sources based on the reviewed conversations.
Use cases
The scenarios below are conceptual examples, not delivered implementations.
- Home appliances online store
Order status and returns
The agent checks the delivery status with the courier, explains the return steps from the store's policy and transfers exchange requests for faulty products to a colleague.
Conceptual example - Local internet provider
Questions about invoices and subscriptions
The agent answers about payment deadlines and packages based on approved documents and opens a technical ticket when the customer reports an outage.
Conceptual example - Made-to-order furniture manufacturer
Delivery and assembly scheduling
The agent reports the order's progress from the internal system and collects the customer's availability, which a colleague confirms.
Conceptual example
Frequently asked questions
Do customers know they are talking to an AI agent?
Yes. We recommend that the agent clearly introduces itself as an automated assistant and always offers the option to speak to a colleague.
What happens when the agent does not know the answer?
It does not make things up. It says it will transfer the request, creates a ticket with a summary of the conversation, and a colleague takes over the case.
How is customer data protected?
The agent only sees the data it needs, after a simple identity check (for example, order number and email). Access is read-only wherever nothing more is needed, and conversations are kept in line with your retention policy and GDPR.
Can the agent approve a return or a refund?
Not by default. It can check whether the request meets the conditions and prepare the case, but approval stays with the team.
Which channels can it work on?
Website chat, email and messaging channels that offer an API, depending on what you already use. We agree on the channels during the analysis.
How is it different from the AI assistant for online stores?
The support agent handles customers who already have a question or a problem: delivery, returns, invoices, complaints, tickets. The e-commerce assistant works mainly before the purchase and helps visitors choose a product from the catalogue. A store can use both, drawing on the same approved sources.
How do we get started?
With an analysis of the requests from recent months, grouped by type. Together we choose the types suited to the agent, set the escalation rules and start a pilot on a single channel, with close monitoring.
Can’t find the answer? Write to us and we’ll reply directly.
Want your support team to focus on the hard cases?
Send us the types of requests you receive most often and we will look at what an agent could take on, with clear escalation to people.
Related solutions
- AI voice agent for inbound callsThe first line for high volumes of support and order calls: caller identification, answers from approved sources, a ticket created and a transfer with context.
- AI assistant for e-commerceGuides shoppers to the right product using your catalogue, answers questions about availability and orders, and prepares cart recovery messages only where consent exists.
- RAG systemsAssistants that search the configured sources before answering and show where each piece of information comes from.