Frontline associates handle a constant flow of conversations every day, from checking stock and answering customer questions to coordinating tasks. And most of these exchanges are on traditional voice tools like 2-way radios and paging systems, meaning these exchanges disappear the moment they’re spoken. Hidden inside this voice traffic is incredibly valuable data that, when used to build small language models (SLMs), can power AI tools that truly understand how the store runs and support associates in real time.

What is a small language model?

Most people are familiar with large language models like ChatGPT, Gemini, or Claude. These systems are trained on massive amounts of data and can answer questions on almost any topic. While powerful, they require heavy computing power and aren’t designed with specific industries in mind.

A small language model is trained on a narrower, domain-specific dataset, which makes it faster, less expensive, and more relevant to the environment it’s built for.

In retail, that means a SLM can be designed to understand the language, workflows, and customer interactions that happen every day inside a store.

Instead of trying to know everything, a SLM focuses on knowing your business.

From store chatter to smart data

Frontline voice traffic captures the interactions that define daily store life. A quick price check, a call for assistance, or a request for stock may seem routine, but together they reveal clear patterns: what customers ask most, where associates need help, and how work moves across the floor.

An average retail associate handles over 80 verbal interactions per shift. Multiply that across hundreds of stores and thousands of associates, and you get a powerful dataset representing real operations. That data trains a SLM to respond accurately while learning the retailer’s own language, product references, and workflows. The result is an AI agent that speaks the store’s language and supports associates in real time whether answering questions, guiding processes, or helping operations run more smoothly.

Building a retailer-specific AI foundation

To bring a SLM to life, retailers need reliable ways to capture and organize voice traffic, along with strong data governance to ensure privacy.

SYNQ’s voice AI suite provides that foundation. By capturing, routing, and managing conversations across radios and paging systems, it transforms everyday chatter into structured, actionable data. That data can then fuel a small language model, helping retailers move from using generic AI to building their own specialized tools tailored to the realities of their stores.

Conclusion: retailers can’t afford not to do this

Frontline conversations have always been part of running a store, but with the right tools, they can become something more: training data for smarter, faster operations.

By building small language models from voice traffic, retailers unlock efficiency, accuracy, and store-specific intelligence. The next wave of retail AI won’t come from generic platforms. It will grow from the conversations already happening on the sales floor.