Does your Polish contact center need a custom model?

Suppose a customer opens with “Nie chcę już korzystać z tego pakietu,” meaning “I don’t want to use this plan anymore.” The representative asks, in Polish, whether the customer wants to change plans or cancel internet service entirely. The answer is “Internet chcę zostawić, tylko przejść na tańszy pakiet”: “I want to keep the internet, just switch to a cheaper plan.” The conversation can now move to a plan change. The opening sentence alone did not establish that.

AI tools call this purpose an intent. Recognizing it can help a representative choose the next part of the conversation or open the appropriate form. In this example, a useful system takes the customer’s answer into account instead of continuing to treat the initial “I don’t want” as a cancellation. The representative can discuss a plan change without first correcting the system.

When a question is enough

An ambiguous sentence can look like evidence that a model struggles with Polish. Yet “I don’t want this plan” leaves both possibilities open to a person too. More training examples cannot establish which one this particular customer meant. Their answer supplies the missing information.

When considering a system, look at the short exchange as a whole. Can it leave the intent unresolved, support a clear follow-up question, and use the answer? Marking a request as unrecognized does not provide that conversation by itself. How the interaction continues is part of the application’s design.

Once the customer has said they want to keep internet service and change plans, asking again about cancellation only prolongs the conversation. Useful intent recognition should reduce those unnecessary repetitions. Review both situations that need a question and situations where the customer has already supplied enough information.

What did the model actually misunderstand?

A different problem arises when a model repeatedly reads clear plan-change requests as cancellations. Customers may speak casually, leave sentences unfinished, or refer back to something they said earlier. If a representative can understand their purpose from the same words and context, there is a specific recognition error to address.

For voice calls, first compare the transcript with the recording. Transcription converts speech into the text the intent model then analyzes. Dropping “nie,” meaning “not,” from “Nie chcę rezygnować z internetu” reverses its meaning: the customer said they did not want to cancel internet service. Adapting intent recognition around an incorrect transcript does not fix the speech-to-text error.

Context can be missing too. If the application passes only the customer’s final “tak,” or “yes,” without the question they answered, first address how the conversation reaches the model. A correct transcript and sufficient context are needed to assess whether the model understands the request.

Does Polish service require a custom model?

Start with the current tool or an existing multilingual model. Microsoft lists Polish among the languages supported by Conversational Language Understanding, its service for recognizing intents in customer utterances. That makes it an option to assess; its usefulness still needs to be checked on the company’s own conversations. Source: Microsoft CLU language support.

The trial should also establish whether the company clearly distinguishes a plan change from ending service. If representatives assign the same kind of request to both categories, agree on their meanings first. For a small set of clearly defined requests, a simpler menu or an improved existing dialogue may be sufficient.

Model adaptation uses examples with the correct purpose of each request identified. The aim is to help the model recognize how this company’s customers express themselves. It becomes relevant when an existing model still confuses particular, understandable requests despite clear categories and adequate context. Polish-language service alone does not establish a need for adaptation.

Compare results on separate test conversations

Compare mistakes on plan-change requests separately from mistakes on cancellations. Strong results on frequent questions about available plans can conceal errors when customers want to end service. Microsoft describes evaluating each intent separately and reviewing which categories the model confuses.

Compare the existing and adapted models on separate Polish test conversations that were not used for training. For our example, the question is whether the system recognizes a plan change after the customer’s answer and avoids returning to cancellation. Representatives can also judge how many incorrect suggestions they have to fix and whether follow-up questions move the conversation forward.

Assess phone and chat separately if the system will serve both. A chat result does not test the transcription stage that phone calls require. Changes to offers or category meanings also warrant another look at recognition. Assigning completed support tickets to teams is a separate task, covered in our article on fine-tuning for support ticket routing.

Discuss a specific recognition problem with Syntalith

Syntalith builds AI applications and adapts models. For a contact center, we propose starting with one distinction the current tool regularly gets wrong. We examine whether the issue is transcription, access to earlier parts of the conversation, or intent recognition itself. A comparison with an existing model helps establish whether an adaptation trial is justified.

The selected approach can connect to the representative’s tool or the dialogue that gathers the reason for contact. Start the first conversation with one misunderstood request and describe the work needed to correct it. If conversation materials would help, we will agree on how to use them. Offer information is on Syntalith’s pricing page.