Capture the enquiry
Take it in from email, portal or website chat.
AI in customer service: Classify requests faster and make targeted use of support knowledge.
Enquiries from email, portal or chat are recognised, linked with customer and knowledge data and prepared as a reply or next support step.
In short: The solution helps your support team with triage, knowledge search, draft replies and ticket management. Standard cases can be automated further. Escalations, goodwill gestures, contract questions and critical commitments remain with the responsible team under defined rules.
Starting point
Where time is lost today.
- Repeated questionsThe same requests are answered from scratch every day.
- Scattered knowledgeSupport knowledge sits in manuals, tickets and people’s heads.
- Wrong assignmentTickets land with the wrong team and get passed around.
- Variable qualityThe answer depends on who happens to be on duty.
- Missing contextWhat was discussed at the last contact isn’t visible.
How it works
What the solution does, step by step.
Match customer and ticket
Link the existing customer, contract and open tickets.
Identify the request
Category, urgency and responsible team according to your rules.
Search knowledge
Find relevant passages in approved support sources – with the source.
Prepare a reply or step
Suggest a draft, a follow-up question or the next processing step.
Document or escalate
Update the ticket; hand defined cases over to people.
Before – after
What changes in day-to-day work.
Examples from the workflow – no percentages, as only a measurement in your business can provide those.
| Situation | Today | With the solution |
|---|---|---|
| Standard question | typed from scratch every time | draft from approved support knowledge, a person checks it |
| Mapping | manual, often wrong | rule-based, linked to customer and contract |
| Context | searched for in the old ticket | previous contacts visible |
| Escalation | gut feeling | defined rules, traceable |
Benefit
What this use case improves.
Faster triage
Category, customer and team are clear early on.
More consistent replies
The same sources, the same rules.
Better use of knowledge
Support knowledge is found instead of rewritten.
Less ticket effort
Upkeep and documentation are prepared.
In action
What working with it looks like.
Sample images from our visual library – the interfaces shown are illustrations, not client data.
Responsibility
What stays with people.
Escalations, goodwill gestures, contract questions, complaints and any commitment with cost implications. Standard cases are prepared; sensitive decisions stay with the team.
The solution prepares, sorts and hands over – your staff make decisions and grant approvals, following rules you set.
Systems & integrations
Where the solution connects.
For example – what exactly gets connected depends on interfaces and permissions and is checked in the AI potential assessment.
- Helpdesk
- CRM
- Email
- Website chat
- Knowledge system
- ERP
Building blocks & products
The technology behind it.
Solution building blocks are the technical components the solution is built from. Products are only named where they fit the task.
RAG / knowledge systems
Make support knowledge usable with sources.
View building blockAI agents
Triage, drafts, ticket upkeep, escalation.
View building blockProcess automation
Handover to helpdesk and CRM.
View building blockCall Agent
If the phone channel is part of it.
View product
Control, data & operation
Control stays part of the system.
Depending on the product and interfaces, we run the solution on your premises, in a Swiss cloud or as a combination. Before the pilot, we clarify data flows, access rights and compliance requirements. Background: Private AI and On-premise & hybrid (running on your own servers or a mix of both).
| Topic | Implementation |
|---|---|
| Data | Customer, contract and ticket data – confidential. |
| Approvals | Escalations, goodwill gestures, contract questions and critical commitments go to the responsible team; automated replies only for defined standard cases. |
| Roles & permissions | Who sees customer data, who approves replies, who changes rules. |
| Logging | Enquiry, sources used, draft, approval and escalation traceable for each ticket. |
| Operating model | Private cloud or hybrid; the knowledge system can run on-premise, the chat channel is checked in the project. |
Industries
Where this use case is typical.
- Manufacturing
- Real estate
- Healthcare
- cross-industrythe workflow is not tied to a single industry
Standard approach
How a trial run (pilot) can start.
The pilot does not cover the whole process straight away. We pick a clearly defined part, set approvals and success criteria and test it with real data and the people who work with it. This approach is the same for every use case.
- Choose a processA clearly defined part of the workflow that takes up time today and can be measured.
- Clarify data and systemsWhich data, which interfaces, which permissions – and what is missing.
- Implement a defined workflowWith defined approvals and success criteria, not the whole process at once.
- Measure in real operationWith real data and the people who work with it; then expand or stop.
Related use cases
Related pages.
Frequently asked questions
Frequently asked questions: customer service & support
Can support run fully automatically?
Only for clearly defined standard cases. Escalations and sensitive decisions need clear limits.
Can the AI take customer data into account?
Yes, if roles, permissions and connected systems allow it.
How do you prevent wrong answers?
Through approved knowledge sources, showing sources, limiting the scope of answers and escalation rules.
Which companies is this use case suitable for?
Companies with a support or service team, documented product or service knowledge and recurring enquiries across several channels.
Next step
Check this task for your business.
Describe your current workflow in two sentences. We check the benefit, data situation, integration effort and risks and show whether a product, a solution building block or a custom solution makes sense.