Anonymized client case · Evidence-grounded customer support

Personalized support grounded in approved knowledge, with urgent cases sent to people.

A specialist service provider needed to answer recurring customer questions across a long treatment journey without allowing an AI system to diagnose, improvise, or hide uncertainty.

Languages
English, Dutch, German
Knowledge
Approved internal documents
Personalization
Journey and timeline aware
Urgent situations
Human escalation

01

The challenge

Customers asked different questions depending on where they were in their journey. Staff repeated guidance, but answers still needed approved sources, customer context, and a safe route for urgent situations.

02 · Interactive workflow

What changed

Stage 1 / 4System stage

Retrieve approved knowledge

Search internal documents first and keep the answer connected to the relevant source.

Stage 2 / 4System stage

Use customer context

Adapt the response to language, profile, date, and current phase of the customer journey.

Stage 3 / 4System stage

Separate information sources

Clearly distinguish internal guidance from approved website information and broader external context.

Stage 4 / 4Human handoff

Escalate urgent messages

Detect warning signals and send the original customer message to the human support team without generating a diagnosis.

03

Measured impact

The delivered system could provide consistent phase-specific support in three languages while making the source of the answer visible.

Evidence and context

It also created a direct path for urgent messages to reach people with the relevant customer context. No time-saving percentage is claimed because live support-volume data was not supplied.

04

Important context

The agent was designed to inform and support, not diagnose. Potentially serious messages triggered human escalation, and the support team remained responsible for medical or professional advice.

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