More and more companies are automating their service.

Today it is normal to enter a website and find:

• virtual assistants
• support chatbots
• commercial bots
• conversational systems with AI

And in many cases, these chatbots are no longer something “secondary”.

👉 are a direct part of the business operation.

In industries such as:

• automotive
• insurance
• banking
• retail
• health

many customers already interact first with a chatbot rather than a person.

The problem that almost no one is considering

Companies monitor:

• servers
• applications
• APIs
• infrastructure

But very few are monitoring anything critical:

👉 whether your chatbot is actually working properly.

Because it’s one thing for the chatbot to be “on”.

And it is quite another thing:

• respond well
• understand correctly
• complete the expected flow
• provide useful information
• achieve the business objective

👉 therein lies the real challenge

The new operational problem

With the advent of more advanced chatbots and even generative AI models, a new complexity appeared:

👉 the answers are no longer completely rigid.

The chatbot may respond differently each time.

And that makes it much more difficult to validate whether it’s really working well.

For example:

• can answer something incoherent
• getting stuck in a conversation
• not offering products correctly
• do not refer to an executive
• failure to complete a key flow

And many times:

👉 no one notices until a customer complains

This is where dVirtualUser comes in.

dVirtualUser is a dParadig software specialized in synthetic monitoring and digital experience.

And today it incorporates a very powerful capability:

👉 monitor chatbots in a conversational manner.

It is not only valid if the site is on top.

Validate if the chatbot really fulfills its function.

How does it work?

Instead of asking fixed questions expecting exact answers:

👉 dVirtualUser naturally converses with chatbot

The robot can:

• write messages
• interpret answers
• advance in the flow
• to try to meet a specific objective

👉 as a real user would do it.

What can be validated

For example:

• if the chatbot responds
• if correctly derived
• if it provides consistent information
• if it is able to complete a commercial flow
• if you understand common questions
• whether it offers products or services correctly

👉 validating real experience, not only availability

Use case: automotive company

Imagine a company that sells vehicles.

Your chatbot helps:

• quote cars
• answer questions
• coordinate test drives
• refer sales executives

Now imagine the chatbot:

• stop recommending models
• answers incorrect information
• no sales referral
• is caught in a flow

👉 the company would probably lose business opportunities without knowing it.

dVirtualUser can:

• start a conversation automatically
• request a quote for a vehicle
• advance through the flow
• validate that the chatbot responds correctly

And if something goes wrong:

👉 generate an alert immediately

Use case: insurance

Another very real example.

Many insurers already use chatbots for:

• quote insurance
• report claims
• answer frequently asked questions
• to guide customers

Now imagine that:

• the quotation flow stops working
• bot delivers incomplete information
• an integration fails
• the chatbot stops deriving correctly

👉 the impact can be enormous

With dVirtualUser you can validate continuously:

• if the flow continues to operate
• if the chatbot responds correctly
• if it achieves the expected goal

👉 just like a real customer

And what happens when something goes wrong?

Here is one of the most important parts.

When dVirtualUser detects a problem:

👉 can generate automatic alerts with visual evidence

For example:

• screenshots
• videos of the conversation
• error context
• detail of the failed flow

👉 making diagnosis a lot easier

This becomes even more important with AI

The smarter chatbots become:

👉 the more difficult it is to monitor them with traditional methods.

Because it is no longer enough to validate:

“if you answered exactly this”

Now you need to validate:

👉 whether it correctly met the conversational objective.

What is important in the background

Chatbots are no longer just an add-on.

In many companies:

👉 are an active part of the customer experience.

And if they are part of the operation:

👉 they should also be monitored like any critical system.

What changes when you do this right

When you actively monitor your chatbots:

• you detect problems before the user does
• valid real experience
• reduce silent failures
• improvements in digital continuity
• protect commercial and customer service processes

👉 especially in channels that today operate 24/7.

If today your company relies on chatbots for customer service, sales or support, there is probably already a new need for monitoring that simply did not exist a few years ago.

👉 dVirtualUser allows you to monitor chatbots conversationally and automatically, validating that they actually meet the expected objectives and quickly alerting you when something stops working correctly.