7 September 2026
Telco firms collect huge volumes of customer feedback, from NPS and satisfaction surveys to the call transcripts generated every day by contact center teams. The challenge is being able to turn that unstructured feedback into reliable answers quickly and being confident in how those answers were reached.
That’s where Zayker comes in. Zayker is a chatbot interface to the Relative Insight platform, giving teams a simple way to query the insights and reports already analyzed in the platform.
Unlike a general-purpose large language model (LLM), Zayker’s answers only draw upon data verified by Relative Insight’s platform. The result is a faster way to explore customer intelligence without sacrificing analytical rigor or accuracy.
Putting Zayker to the test with telco data
To illustrate how this works in practice, Relative Insight Customer Success Director Sergio Gomes demonstrated how a telco firm can use Zayker to pinpoint customer intelligence.
In this example, Sergio loaded three months of a telco firm’s voice of customer data into Relative Insight. This included NPS and satisfaction surveys, alongside call transcripts from conversations between customers and support agents in contact centers.
Sergio examined the data from the perspective of a regional lead. In this example, the regional lead’s area is underperforming and they want to understand why. Traditionally, answering that question might involve going back through reports, analyzing different datasets and manually piecing together the story.
With Zayker, they can simply ask:
“Why is Denver scoring lower than the rest of the network?”
The answer arrives almost instantaneously. Zayker identifies network and signal as a core regional complaint, while also citing the reports from which the insight was drawn. That means the regional lead isn’t simply presented with an answer — they can see where the underlying insight came from and review the source themselves.
Tracing the story across all data sources
Reporting on isolated anecdotes in one part of customer feedback won’t move the needle. The next question the regional lead needs to answer is: Does the same issue appear elsewhere in the customer experience?
To identify this, Sergio asks Zayker whether the network and signal issue also appears in call transcripts. Zayker quickly confirms that the observation is consistent across the different sources, again citing the underlying reports.
This is particularly valuable for telco businesses, where customer sentiment can be spread across multiple feedback channels. A problem identified in survey data becomes much more actionable when teams can establish that customers are raising the same issue in their conversations with support agents.
Zayker offers rapid, defensible insight for telcos
Why is it that you can trust Zayker’s outputs, but not those of generic LLMs? The key difference is what sits behind the chat interface.
Because Zayker draws on analysis that has already been run through Relative Insight, the themes it surfaces have already been verified. Ask the same question tomorrow, or have a colleague ask it, and the answer will be backed by the same underlying metrics.
That consistency matters. With generic LLMs, asking the same question twice will produce different answers. Zayker is designed instead to provide consistent, defensible answers grounded in the same underlying analysis.
For a telco leader, that makes an important difference. When challenged on a finding, teams don’t have to defend a model’s guess. They can show the work behind the insight.
And because Zayker is purpose-built for querying this analyzed data, it can also run at a fraction of the cost of general-purpose LLMs.
For teams that already trust Relative Insight to show them what customers are saying, Zayker is the next step: a faster, more accessible way to get to the answers contained within unstructured customer data.
