Liquid's multimodal models run entirely on the CPUs and NPUs already in your production vehicles. Natural voice interaction, cabin vision, and personalized agents are achievable right now, with no cloud dependencies or new hardware.
On their own devices, drivers are accustomed to LLM-grade interfaces. In their cars, they're still getting legacy reactive voice. That's not premium, and it's not the future of luxury. In-car AI is.
Automakers struggle to deploy rich in-vehicle AI assistants because:
LLMs are designed for data centers, not chips behind a dashboard.
Reliability, latency, and privacy concerns are critical for in-car systems.
They lack the sophistication to handle natural, multimodal interactions across the cabin.
SLMs are tuned for low-memory budgets in both CPUs and NPUs. That means sub-second responses for infotainment, navigation, and safety prompts on existing SoCs.
Agents are designed from the ground up to process both vision and natural language audio. So they understand intent, emotion, and cabin context, and use it to call hundreds of your vehicle functions.
Unique architectures combine the best of edge- and cloud-based AI. You decide which features to run at the edge versus the cloud, and how each operates on your vehicles.
Liquid SLMs can power in-car voice assistants that rival cloud-based AI.
A single model, on existing hardware, running in 20+ languages, at 5x the speed of existing solutions.
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