Real‑time multimodal voice AI platforms bring together low‑latency speech‑to‑text, large language models, contextual multimodal inputs and text‑to‑speech to power live voice agents, transcription, voice cloning and conversational automation. This topic examines general‑purpose providers (Meta’s Muse Spark, OpenAI, Google Gemini) alongside specialist vendors and embedded solutions that target contact centers, professional services and productivity workflows. Relevance (2026‑05‑15): enterprises and service providers are prioritizing production‑grade voice stacks that can operate in real time with privacy, cost and latency constraints. Advances in multimodal models, streaming APIs and on‑device inference have made live voice agents and automated call handling commercially viable. At the same time, demand for accurate transcription, expressive TTS and ethical voice cloning is driving a market of focused alternatives. Key tools and roles: Google Gemini supplies multimodal generative models and developer APIs via Google AI Studio/Vertex AI for integration into workflows; OpenAI’s voice and multimodal capabilities offer developer APIs for streaming conversation and synthesis; Meta Muse Spark targets low‑latency, multimodal voice experiences (model details and deployment options vary by provider). Specialist vendors include ElevenLabs (high‑fidelity TTS, voice cloning, transcription), ZenCall.ai and Vocea (real‑time AI phone agents and service‑oriented voice assistants), Hona (legal‑practice client reception and case communications), and lightweight transcription/note apps like Talknoto, SpeakPen and Milapole.com. What to compare: latency and streaming quality, transcription accuracy, TTS naturalness and voice reuse controls, integration with LLMs and CRM systems, deployment options (cloud vs on‑premise/on‑device), pricing and compliance features. Together, these dimensions define whether a platform suits contact centers, professional services, or embeddable consumer tools.