Moshi AI vs Ad Auris Play

Moshi AI is a speech-native conversational model from Kyutai, a Paris-based open-science research lab. Instead of chaining speech recognition, text generation, and text-to-speech, Moshi processes audio directly and holds full-duplex voice conversations with minimal latency.

Its multi-stream design runs separate channels for the user, Moshi's spoken output, and an Inner Monologue text stream that improves coherence. That setup lets Moshi listen and talk at the same time, handle overlaps, interruptions, and backchanneling like a real conversation rather than rigid speaker turns.

Moshi is built on Helium, a 7B language model, and Mimi, Kyutai's neural audio codec. Weights and inference code ship for PyTorch, Rust, and MLX, and you can try it in the browser at moshi-chat.kyutai.org. Researchers, voice AI developers, and anyone building real-time spoken interfaces will find the most value here.

Ad Auris Play turns written marketing content into branded audio for B2B teams that want listeners to become sales leads. You sync blogs, newsletters, LinkedIn posts, or case studies, and the platform drafts an audio-first script before generating narration with 50+ voices across multiple languages. Finished audio embeds on your site or distributes to Spotify and YouTube.

Most text-to-speech tools stop at narration. Ad Auris Play adds a listener identification pixel that ties playtime back to CRM contacts in HubSpot, Salesforce, or Pipedrive, then generates short personalized audio snippets for outbound email and LinkedIn. That sales-intelligence angle is the differentiator, though it means the product targets revenue teams rather than casual readers.

Marketing and sales teams at B2B companies use Ad Auris Play to repurpose existing content, identify high-intent listeners, and book meetings through audio CTAs with calendar links. Pricing is not published on the site; you book a call to get started.