01 / Readable
Readable
The draft shows what information was used and where it came from.
Digital employees Support
Automate customer service with a digital support employee.
Automating customer service doesn’t mean letting AI answer every ticket unchecked. Often the better start is: read the request, look for context, create a draft answer, let people decide.
A digital support employee reduces research, FAQ handling, and preparation work without letting sensitive answers, edge cases, or customer data slip out of control.
Process view Sam in the ticket view Example view: Sam recognizes the case type, reviews contextual sources, and prepares a draft response that a human can take over or escalate.
Screenshot of a ticket view in which Sam analyzes a login case, cites sources, and prepares a draft response for approval. 01 What it takes off the team
Automating customer service starts before the response.
A lot of time is lost before a response is written: searching for old tickets, checking customer data, reading internal rules, gathering context from multiple systems.
- 01
Read and classify tickets
- 02
Find appropriate context from knowledge sources and systems
- 03
Prepare draft answers
- 04
Automate FAQ cases within clear boundaries
- 05
Escalate unclear cases with context
- 06
Make research results visible to people
02 Approach
Not a chatbot that has to answer everything.
Chatbot responses are too limited
Many support projects start with the wrong expectation: the AI should immediately answer every request itself. This seems simple in a demo and quickly becomes risky in operation.
A digital support agent can first start as a research and draft system. If certain types of requests are stable, more will be automated. Everything else remains with humans.
Good automation has clear limits
When questions clearly recur, a digital support employee can take on more responsibility: identify FAQs, provide appropriate answers, and escalate anything outside the scope.
This works particularly well when knowledge sources are clean and the team has defined which answers may be automated.
03 Example from a project
Sam creates answer drafts with the research context.
Sam works in a ticketing system. It reads a request, looks for the required context across multiple systems, and creates a draft response. To do this, it provides the research result so that a human can check and send the answer.
- 01 · Input
A ticket arrives in the support system.
Source and category are defined in the pipeline.
- 02 · Reading
Sam reads the request and identifies what information is missing.
What is being asked, what is context, what is open?
- 03 · Research
Sam looks for the right context in the relevant systems.
Knowledge base, CRM, contract and order data, old tickets.
- 04 · Draft
Sam creates a draft answer based on research.
This makes it clear where the information comes from.
- 05 · Approval
A human checks, adjusts and sends.
For FAQ cases, approval can be automated later.
Sam is not an autopilot in this setup. Above all, Sam takes the search work off the team.
04 visibility
The employee must stay visible.
Especially in support you want to see what happens: Which request was read? What context was found? What does the digital employee suggest? Why was it escalated?
02 / Controllable
Controllable
People make decisions when there is risk, uncertainty, or edge cases.
03 / Understandable
Understandable
Logs and escalations make work traceable for each case.
05 Security
Security belongs in the workflow.
A digital support employee works with customer data. That is why it needs boundaries.
01
EU hosting
Backend and used LLMs run in the EU.
02
Roles and rights
Per system, per data type and task.
03
Audit logs
Every source read, every draft, and every escalation is traceable.
04
Human-in-the-Loop
Standard for risk, edge cases, and uncertain answers.
05
No provider training
Customer data is not used to train third-party LLMs.
We explain the basic security architecture on the security page. The category behind it is described under digital employees.
06 Approach
This is what a first pilot can look like.
We start with a clear support workflow. The digital employee gets a name, a visible identity, access to the necessary knowledge sources, and an initial task in the ticket process.
The first step is not full automation. It is the recurring cases that can be drafted so cleanly that the team can review and send them with minimal changes.
Once the flow is stable, we expand: more FAQ cases, more channels, more automation. Step by step.
07 Frequently asked questions
Frequently asked questions.
Can AI automate customer service?
Yes, but not as an open autopilot. A good start is often a draft with research results. The team checks and sends the response. More automation comes later, once request type, knowledge source, and escalation rules are clear.
Which customer service requests are suitable for automation?
Recurring questions, clear FAQ cases, similarly structured inquiries, and tickets that require context from existing systems are well suited.
Can a ticket system be connected?
Many ticket systems can be connected if interfaces, data access, and rights are suitable. Current customer projects include Zendesk and Zoho Desk setups.
How are incorrect answers prevented?
Through limited tasks, verified knowledge sources, draft responses instead of autopilot, escalation rules, audit logs, and human approval for risk cases.
When does it escalate to people?
If the case is outside the defined scope, data is missing, the answer is uncertain, or a decision requires human responsibility.
What happens to customer data?
Customer data is not used to train third-party LLMs. The backend and deployed LLMs run in the EU. Specific data processing is documented for each setup.
Start a conversation
Do you want to know whether your customer service can be automated in a controlled manner?
Talk about a support pilotYuno asks four short questions about request volume, knowledge sources, risk cases, and approval. After that, it is clearer whether a support pilot makes sense.