A Customer Service representative opens their inbox in the morning and sees a dozen emails with technical inquiries. What's the IP rating? What are the fixture dimensions? Is a catalog sheet available for this series? Each question requires the same steps: find the right product in the catalog, access the PIM or open the appropriate PDF, extract the data, and write a response. With just one email, it takes a few minutes. With dozens a day, it's a significant portion of the day.
The AI agent doesn't send the response itself. It does something else: before the customer service representative opens the email, the agent has already searched the PIM, found the right product, extracted technical data, and prepared a ready-made draft response. The employee opens the message and, instead of starting from scratch, verifies the draft and clicks "send."
This isn't an exceptional scenario. It's a common occurrence for e-commerce companies with extensive product catalogs – especially in industries where B2B customers buy based on technical specifications, not images. Every question about parameters, dimensions, availability, or documentation takes time and knowledge, often stored in the PIM system – available but requiring a retrieval each time.
An AI agent for Customer Service that responds to product inquiries is the answer to this specific problem.
At some point, you're faced with the choice of whether to hire more people for your sales department. And if not, what should you do instead?
This is where an AI agent comes in. It reads emails, searches for products in the catalog, and writes a ready-made response before the salesperson even opens the message. What previously took dozens of minutes can now be ready in just a few seconds.
In this article, we show how offer automation based on the AI Assistant works – using the example of an implementation we completed for the DobreGniazdka.pl, LuxMarket.pl and OtoLampy.pl brands.
What emails arrive at the Customer Service Office most often and why is it a problem?
Technical inquiries have their own specifics. Customers often don't know the manufacturer's terminology and describe the product in their own way, based on what it looks like or where they've seen it.
Handling such an email requires: finding the product in the catalog, checking the technical data sheet, verifying availability, and writing a response in a way that the customer understands. For simple questions, this takes a few minutes. For complex configurations, it takes longer, as it requires the knowledge of a specialist, not just a customer service representative.
The problem grows with scale. With a few hundred queries per month, this can be handled manually. But with a few thousand, it becomes pointless.
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Where does the AI agent get answers to technical questions?
The answer to a technical question must be precise. If the AI Agent provides incorrect dimensions or an incorrect IP rating, the customer purchases the wrong product and the problem returns, this time as a complaint.
That's why the agent doesn't "invent" answers. It retrieves them directly from the PIM—the product information management system—where technical data, attributes, catalog cards, instructions, photos, and certificates are stored. The PIM is the source of truth about the product, and the AI agent is the tool that delivers this truth to the customer in response to their specific question.
In the implementations we implement at Cognize, the agent connects to the PIM via API and searches the vector database—that is, it searches semantically, not just by keywords. This allows it to understand that a customer asking about "that white socket with a round frame" may be referring to a specific series from the catalog, even if they don't know its name.
If ERP is also connected to the system, the Agent can also check the current availability of the product and inform the customer whether it is in stock.
We write more about how PIM works as a foundation for product data management in the article Introduction to PIM: for whom, why and how does it help.
How does Agent AI work in practice? From product inquiry to response
We will show a specific flow of information using the example of an implementation carried out for Ostrowski Handel Internetowy, supporting the brands DobreGniazdka.pl, LuxMarket.pl and OtoLampy.pl.
A customer sends an email asking about the technical details of a specific product – say a lighting fixture – and asks about the beam angle and whether it is suitable for installation in bathrooms.
What does Agent AI do?
- Classifies intention. It recognizes that this is a technical inquiry with no intention of buying "here and now" - so it does not create a basket or an offer, but prepares a response with information about the product.
- Identifies the product. It extracts features described by the customer from the email and matches them with the appropriate product in the database. If the customer's description of the product is ambiguous, the agent can ask for the model number or additional details – instead of guessing.
- Downloads data from PIM. Downloads technical parameters: color temperature, luminous flux, beam angle, IP rating, energy class, dimensions, and weight. If a data sheet is available, a link to the file is included.
- Prepares a draft of the response. The Customer Service representative sees a finished draft email in Gmail – complete, precise, and with specific details. They can approve it immediately or make further adjustments. They remain in full control of what they're sending.
The time it takes for a Customer Service employee to verify the draft and click "send" is just a few seconds. No need to open a catalog card, search through the PIM, or ask a specialist.
Where do the AI Assistant's capabilities end?
An AI agent is good at answering questions that require data for answers. It's less good at answering questions that require interpretation or business context.
Several situations where it is worth forwarding the entire inquiry to a Customer Service employee:
- Questions about custom configurations. If a customer asks if a product can be adapted for a non-standard installation or a use not intended by the manufacturer, the AI Agent can only provide data from the technical data sheet. A decision on whether this will work in a given case requires a specialist.
- Questions with the context of previous conversations. An agent doesn't "remember" a customer's correspondence history unless they're integrated with CRM. If a customer refers to arrangements made a month ago, a customer service representative is irreplaceable.
- Ambiguous situations. If the AI Assistant is unable to match the product with sufficient certainty, the system should redirect the message to an employee, and in the model we are implementing, this is exactly how it works. The customer service employee receives information that the Agent did not find a clear match and requests manual handling.
This is the Human-in-the-Loop model: The AI assistant handles what it can handle correctly and passes on what it shouldn't handle on its own.
What does this change for the BOK team?
Implemented for Ostrowski Handel Internetowy, the AI assistant handles approximately 3000 messages per month. The time required to respond to emails has been reduced by over 50%, and over 80% of responses are so accurate that they don't require additional customer service work.
From the Customer Service team's perspective, the change looks like this: instead of starting each email from scratch, searching for a product in the catalog, and writing a response, the employee opens a draft prepared by the Agent, reviews it, and clicks "send." For simple technical questions, this takes just a few seconds instead of dozens of minutes.
An important side effect: standardization. Every response to a technical question is delivered in the same format – with data directly from the PIM, eliminating the risk of different employees providing different values for the same parameter because they used different versions of the datasheet.
More details about this AI Assistant implementation can be found in the Ostrowski Handel Internetowy case study.
What condition must be met for this to work?
An AI agent is only as good as the data it uses. If the PIM is incomplete—missing attributes, outdated catalog cards, or descriptions taken out of context—the agent will either provide incorrect data or provide no data at all and forward the query to a human agent.
Implementing an AI Agent to handle product queries makes sense when:
- product data in PIM (or in a system performing such a function) is complete and up-to-date for the product categories that generate the most inquiries,
- the catalog is large enough that manual handling of technical queries is a real burden for the team,
- The Customer Service Office is willing to change the work model from "I write a response" to "I verify the draft".
If PIM is just being implemented or your data is chaotic, it's worth starting by organizing this foundation. We can help you assess whether and when implementing an AI agent makes sense – we encourage you to schedule a free consultation to discuss your current situation.
And if you're just considering PIM, check out our resources on PIM implementations or start with our introductory article.
Summary
Technical questions in the Customer Service inbox are one of those areas where automation brings quick, measurable results – provided that the product data is in order.
An AI agent integrated with PIM and ERP can answer questions about dimensions, availability, or catalog cards precisely and immediately—without involving a specialist. A customer service representative simply verifies the draft and sends it. Response time is reduced while quality improves.
This isn't a tool for every company at every stage of development, but if you manage hundreds or thousands of queries per month, it's worth exploring what exactly an AI assistant could accomplish for you. We encourage you to fill out the contact form below to discuss your company's situation.
