Use case
AI customer support employee
Customer questions pile up while your team loses time to searching, triage and standard answers. Let routine questions be prepared or handled directly, and keep human attention for exceptions and escalations.
ApproachAI employee
Industry
All sectors
Type
Employee
Deployment
Support, ready for review
Connected systems
Sound familiar?
Are you running into this too?
Support is often the first thing to buckle as you grow. Questions pile up, expectations rise (a fast answer, ideally 24/7, and not having to repeat your story twice), and your team spends much of the day reading, searching and routing instead of with the customer itself.
The work is also fragmented: the right answer is scattered across knowledge articles, past tickets and separate systems. As a result answers differ per employee, tickets pile up when it gets busy and waiting times grow. Not because your team is not working hard, but because too much routine work is done by hand.
That kind of work, reading, sorting, looking things up and drafting a first reply, is exactly what suits an AI employee. Not to replace your team, but to take over the routine, so your people have time for the conversations that really need attention.
The solution
What does the solution look like?
This AI employee answers recurring customer questions, triages tickets and finds answers in your knowledge base and customer data. It connects to all of your own systems, from CRM and ticketing to email and knowledge base, so it works with the context you already have. Routine work is prepared or handled automatically, while your team stays in control of exceptions, sensitive communication and escalations.
- Customer questions arrive by email, chat or the ticketing system and are read and sorted by hand.
- Your team digs through separate articles, past tickets and customer data, then writes each reply itself.
- When things get busy or unclear, you get waiting times, inconsistent answers and extra escalations.
- The AI reads new tickets right away, spots the topic and urgency and routes them correctly.
- The AI pulls in relevant knowledge and customer context and prepares a reply, summary or follow-up action.
- Only exceptions, sensitive communication and low-confidence cases go through to your team.
Step by step
How does it work?
A customer question comes in by email, chat, form or the ticketing system, often with customer and order context already attached.
The agent classifies the question, finds the best answer in your knowledge base and customer data, and prepares a reply or follow-up action.
Your colleague reviews exceptions, approves sensitive communication and takes over escalations or complex cases.
Work it out yourself
What does this deliver?
Calculated per ticket. Adjust the numbers to your situation.
Net saving per month
Time per month
Per year
Equivalent to
Figures for illustration. In an introduction or demo we work it through with your own numbers.
Here is how we approach this.
You bring in a digital colleague that takes over this work, trained on your own systems and data, with a human keeping an eye on things. Scalable up and down, and you keep control.
No pilot that gets stuck: it runs in production, in your own environment, and grows along with new models, tools and data.
A colleague you train in.
- Trained on your own systems and data
- Scales up and down, you stay in control
- A human keeps an eye on things and stays ultimately responsible
Keep exploring
In the same vein.
AI assistant for proposals and reports
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AI knowledge base with source references
Employees lose time searching, asking around and waiting for the right answer. With an internal knowledge assistant you find policies, processes and document knowledge straight away, with source references.
Curious what this would deliver for you?
In 20 minutes we look together at whether this use case fits and what the first step is.
No sales pitch. 20 minutes about your situation.