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Most customers are multilingual – your restaurant should be, too
A growing share of your customers are placing orders in their native language, or trying to. Take drive-thru as an example: Hispanic Americans are more likely than non-Hispanic Americans to dine at fast food restaurants regularly [1].
In a diversifying world, brands that force a customer out of their comfort zone to order in a language that isn't theirs are going to fall behind. A hit to your customer satisfaction and brand trust, incorrect orders, too-long speed of service. At the very least, they’re walking away with a less-than-optimal taste in their mouth, and you’ve missed out on an opportunity to delight.
If you’re ahead of the curve, you’ve already looked at Voice AI as a solution to this problem, but multilingual Voice AI is a challenge that few platforms have conquered.
Multilingual Voice AI has been begging for reinvention
Most "multilingual" systems are stitched together. Language detection runs first, hands off to a monolingual model for the detected language, and a translation layer mediates between them. The result is high latency, brittle handoffs, and accuracy that falls apart the moment a customer says "and para mi hijo, una hamburguesa con queso y fries." Multilingual customers don't speak in clean monolingual blocks. You need a Voice AI that can accommodate switches mid-sentence, mid-word, mid-thought.
Today we're announcing Flux Multilingual for Restaurants, the version of our restaurant Voice AI that handles natural code-switching between languages without a perceptible latency hit. English, Spanish, French, German, Hindi, Russian, Portuguese, Japanese, Italian, and Dutch: ten languages, one model, one API, monolingual-grade accuracy, built for real-time streaming voice agents.
Restaurant audio is hard, and Flux Multilingual was built to solve it
Restaurant tech is one of the harder Voice AI environments to build in. You have wind, engines, kitchen noise, multiple drive-thru systems next to each other, the nearby highway, music and background conversation in the car, accents that vary by region, customers who don't want to repeat themselves, and a POS that wasn't designed to talk to an AI in any language. Adding multilingual support without a clear strategy makes everything slower.
We built Flux Multilingual on the same research stack that runs our English restaurant Voice AI product, which is in production at brands you know and order from. The infrastructure, reliability target, and price-per-conversation are the same as the English-only product. Adding multilingual coverage doesn't double your cost or latency.
If you're a CTO or engineering lead deciding whether multilingual Voice AI is a real moat or a roadmap checkbox: it's the moat. Customers who can order in their first language come back, and the ones who can't, churn quietly to the chain on the next exit.
The voice model is the easiest part
Already using Flux English? Change your model to flux-general-multi. Same API, same integration – just change one parameter, and gain access to English, Spanish, French, German, Hindi, Russian, Portuguese, Japanese, Italian, and Dutch.
If you're not, trust us – onboarding the multilingual model is the easy part. The hard part is something only Deepgram for Restaurants can do: injecting voice AI into their veins of your operation, making it work with POS systems, menu data, and tricky deployments. And we’re up for the challenge. Reach out – hablemos – let's talk.
Start building today:
- See the live demo →
- Try it in the Playground →
- Get started with the API →
- Build end-to-end with the Voice Agent API →
- Sign up for an API key →
- Explore the documentation →
- Join our Discord community →
[1] "Key QSR Insights Among U.S. Hispanic Adults." CivicScience, civicscience.com/key-qsr-insights-among-u-s-hispanic-adults/
