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Solution Brief

Transforming Vehicle Ownership Experiences through SDV Technologies and AI

The vehicle industry is rapidly transitioning towards Software-Defined Vehicles (SDVs), turning vehicles into powerful digital platforms. While modern vehicles boast advanced technology, their complexity often complicates ownership and after-sales service experiences. Sonatus’s AI Technician Builder transforms this complexity into simplicity, delivering personalized, intuitive, and efficient vehicle maintenance and repair experiences through Generative AI (GenAI), real-time vehicle data analytics, and automated actions.

Download this Solution Brief to learn how OEMs can leverage SDV technologies.

  • Challenges of Modern Vehicle Ownership
  • Sonatus AI Technician Builder: Vehicle-Specific Maintenance and Diagnostics
  • Technology Behind the AI Technician Builder

 

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Frequently Asked Questions

The Sonatus AI Technician Builder is a no-code platform designed for Original Equipment Manufacturers (OEMs). It empowers automakers to create customized "AI Technicians" (intelligent chatbots) that provide personalized vehicle support. These AI agents are tailored to three specific user groups:
- Vehicle Owners: To simplify vehicle operation and provide instant answers to questions.
- Service Technicians: To streamline diagnostics, parts ordering, and repair scheduling.
- Diagnostic Engineers: To gain fleet-wide insights and proactively identify patterns or defects.

The AI Technician doesn't just rely on static manuals; it actively pulls live data from the vehicle. It uses:
- Sonatus Collector AI: A tool that dynamically captures real-time data such as ECU signals, Diagnostic Trouble Codes (DTCs), sensor logs, and environmental conditions.
- Sonatus Foundation: An underlying software infrastructure that manages in-vehicle networks and storage, ensuring the AI agent can access the specific signals it needs from various vehicle subsystems.
- Retrieval-Augmented Generation (RAG): This technique combines the live vehicle data with static OEM knowledge sources (like factory service manuals) to generate precise, context-aware troubleshooting recommendations.

Yes, to an extent. The solution offers a flexible hybrid deployment model:
- In-Vehicle Deployment: "Small Language Models" (SLMs) are optimized to run directly on the vehicle's embedded hardware. They are compressed for efficiency and allow the AI Technician to function even when connectivity is intermittent or unavailable, ensuring robust diagnostics at all times.
- Cloud Deployment: When connectivity is available, "Large Language Models" (LLMs) in the cloud can process heavier workloads and access broader fleet-wide knowledge graphs for even deeper analysis.

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