This topic covers platforms and tool categories used to build conversational AI for in‑car and broader automotive experiences: voice assistants, passenger/cabin agents, IVI integrations, and backend CX automation. In‑car systems prioritize low latency, robust speech recognition, expressive text‑to‑speech, offline or edge deployment, multi‑agent orchestration, and privacy/compliance controls. They also require integration with vehicle sensors, backend CX systems, and updateable models via AI marketplaces. Key tool types include conversation intelligence and orchestration platforms (for intent handling, dialogue flows and analytics), voice synthesis and transcription engines (for real‑time TTS, voice cloning and accurate STT), CX automation platforms (to tie in service workflows and telemetry), and AI tool marketplaces (for discovering, evaluating and governing models and voices). Representative tools: IBM watsonx Assistant for enterprise virtual agents and multi‑agent orchestrations; Anthropic’s Claude family for conversational LLM capabilities; Google’s Vertex AI for model training, fine‑tuning and deployment; ElevenLabs, Murf AI and Smallest.ai for production TTS, voice cloning and real‑time low‑latency audio; StackAI for no‑code/low‑code agent deployment and governance; and Anakin.ai for large libraries of prebuilt automation apps. Choosing the right stack depends on constraints—edge vs cloud, regulatory/privacy needs, language and accent coverage, and integration with vehicle CAN/telemetry and CX systems. Current priorities (as of 2026-02-08) are realistic, low‑latency synthetic voices, hybrid on‑device/cloud inference, multi‑agent orchestration, and stronger tooling for deployment governance and lifecycle management.