Use case · AI Studio

Customer service

Tickets enriched with context automatically, staff get well-founded draft replies straight away.

01 · The brief

Replying faster without losing quality.

In service, a lot of time goes into gathering the context: who is the customer, what did they buy, what went wrong last time, what does the contract say? Only then does the actual work begin, the reply.

Because that context sits in different systems, every enquiry takes longer than necessary, and the quality of the answer depends on who handles it.

The brief: an agent team that supplies the context and drafts a well-founded reply, the decision still rests with a person.

02 · What the team does

What the team handles in our own operations.

Enriching context

Every incoming ticket is automatically supplemented with purchase history, account balance, app version and earlier requests. A script the agents built once does this and has been running for cents ever since.

Resolving payment issues

For payment questions an agent checks the back end and the payment provider in parallel and presents the finding with a draft reply, instead of the employee searching two systems.

Narrowing down technical cases

If the case turns technical, the engineering team of the agent network takes over: it follows the trail to the root cause and gives support the draft reply, for example “please update your operating system” instead of an engineering ticket.

Learning from corrections

Approvals and edits by staff flow back as rules. The suggestions become more accurate over time without anyone having to maintain a knowledge base.

03 · How it works

How the agents work.

People stay at the gate: the agents prepare, the team approves.

Step 01

Classify

Incoming enquiries are captured, categorised and sorted by urgency.

Step 02

Enrich

Customer history, orders, contracts and previous cases are added automatically.

Step 03

Draft

A reasoned draft reply is produced, including references to the sources.

Step 04

Learn

Approvals and corrections feed back, and the suggestions become more accurate over time.

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04 · The outcome

What changes.

Handling becomes noticeably faster because the gathering falls away. Staff don’t start from a blank screen but from a well-founded draft.

At the same time quality becomes more consistent: even complex cases come with the full context attached from the start.

  • The context is there the moment the ticket is opened.
  • Draft replies with reasoning and source references.
  • Consistent quality, regardless of who handles the case.
  • Approval stays with a person.

05 · Systems and access levels

Systems and access levels.

Example set-up. The agent only writes where your team allows it.

SystemUsed forAccess level
Support / ticket systemEnquiries, history, statusread · write internal enrichment and drafts
CRM / customer masterCustomer, contracts, contactsread only
ERP / shopOrders, deliveries, invoicesread only
Payment providerIndividual transactions, chargebacksread only
Knowledge baseGuides, templates, rulesread · changes only after approval

06 · Limits

Where the limits are.

The agent does not reply to customers itself. It delivers context and a draft. A person checks and sends.

For technical cases the coordinator brings in the engineering team instead of support guessing. Escalation remains a decision of your team.

The quality of the drafts grows with the corrections: every approval and every edit is recorded as a rule. In the first weeks the share of queries is at its highest.

Frequently asked

Frequently asked questions about this use case.

Does the agent reply to the customer directly?

No. It prepares the case and proposes a reply. Your staff check and send.

Which systems are connected?

Your ticket or support system, CRM, ERP or shop and, where relevant, the payment provider. No new tool and no second inbox is created.

How does the team handle customer data?

Read-only, with the permissions the individual case requires, on servers in Germany. Details are on the data security and hosting page.

From the AI Blog

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