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24 7 Retail agent using GPT Realtime, lessons for UK business teams

A new retail agent example shows how fast 24 7 multilingual customer support can go live, and what to measure when you deploy an AI assistant for real shoppers.

31 July 2026

A diverse group of call center agents working with laptops and headsets in a modern office.
Photograph by Mikhail Nilov · Pexels

A recent case study describes a retail deployment that aims to serve customers at any time, across languages, using GPT Realtime. For business leaders, the main takeaway is not the novelty of chat, it is the operational outcome: a live retail support experience that was tested quickly, then measured directly with real usage and feedback.

What changed in the deployment

The project centres on GPT Realtime powering a retail agent that delivers 24 7 multilingual support to shoppers. The team built and launched it in a short window, then tracked both usage and customer sentiment through survey responses.

What business teams should do next

If you are planning an AI customer support rollout, this is a practical checklist based on how this deployment was evaluated. First, define the service level you are trying to replace, for example around the clock answers for routine questions. Second, design for multilingual support if your customer base needs it, then validate in the languages you expect. Third, pick measurable success metrics before launch, such as active usage volume and the proportion of positive survey feedback. Finally, run the initiative as a time boxed pilot so you can see early signals and decide whether to scale.

Focus on operational KPIs you can get quickly, usage and customer feedback, before you optimise prompts or add more capabilities.

Early results to interpret carefully

The case study reports that 30,000 people used the agent, and 92 percent of survey responses were positive. Use these figures as a benchmark for what to track in your own pilot, but compare them against your baseline support demand and your survey method so you can judge lift fairly.

Risk and governance points for customer facing agents

Even when results look positive, customer facing AI needs operational controls. Start with clear scope, so the agent is responsible for defined support tasks. Ensure escalation paths exist when the agent cannot answer. And treat survey feedback as part of ongoing quality monitoring rather than a one off evaluation.

Next step

Start with the free AI Opportunity Assessment.

A short, no-obligation conversation about where enquiries, hours and revenue leak today. You do not have to pick a tier to have it, and what comes out of it feeds Discover, so the first paid day starts from evidence rather than a blank sheet.