Smart speakers are getting smarter about understanding what you're actually trying to say. Instead of just recognizing isolated voice commands, a new wave of conversational AI systems can now grasp context, regional language nuances, and complex requests in real time. Two major developments show how audio-visual artificial intelligence is reshaping the smart device landscape: boAt's partnership with Google to bring Gemini-powered conversational agents to Indian consumers, and Meta's release of Muse Spark, a multimodal model designed to reason across voice, text, images, and video simultaneously.

What's the Difference Between Old Voice Assistants and New Conversational AI?

Traditional voice assistants work like a vending machine: you press a button (say a command), and it dispenses a single response. They excel at simple tasks like "play music" or "set a timer," but struggle when you ask something nuanced or context-dependent. The new generation of conversational AI systems, by contrast, maintain awareness of what you've already said, understand regional dialects and cultural references, and can handle follow-up questions that build on previous exchanges.

boAt's new Crest AI lineup, built on Google's Gemini Live technology, exemplifies this shift. Unlike traditional voice assistants that handle simple commands, Crest AI is designed to understand context, regional nuances, and more complex queries in real time. The system can engage in natural conversations, manage device control, handle music playback, assist with productivity tasks, provide language translation, and answer frequently asked questions.

"We are moving beyond basic voice assistants to create intelligent, conversational agents for general voice queries, device control, music playback, productivity, language translation and FAQ support," said Gaurav Nayyar, CEO of boAt.

Gaurav Nayyar, CEO of boAt

How Are Companies Building Multimodal AI Into Consumer Devices?

• Integration with Productivity Ecosystems: Crest AI devices connect with Google Workspace, allowing users to access Gmail, Google Tasks, and Google Calendar through voice commands alone. Users can respond to emails or schedule meetings without touching their phones.
• Real-Time Translation Capabilities: boAt is introducing Crest Translate, which enables two-way translation in real time, breaking down language barriers for multilingual users and regional dialect speakers.
• Parallel Reasoning and Tool Use: Meta's Muse Spark model can initiate and manage multiple specialized agents in parallel to solve complex tasks. For example, planning, code generation, data analysis, and synthesis can occur within workflows that are more autonomous and adaptive.
• Extended Context Windows: Muse Spark supports context windows of 1 million tokens, roughly equivalent to processing 100,000 words at once, allowing the model to maintain coherence over extended interactions or within long documents.
• Cloud Infrastructure Underpinning: boAt's integration is built on Google Cloud infrastructure and Gemini Live's multimodal capabilities through the Gemini Enterprise Agent Platform, enabling the company to scale conversational AI to millions of users across India.

The scale of this rollout is significant. boAt expanded beyond personal audio into wearables, charging solutions, audio-visual products, and other lifestyle technology categories, with 71% of the company's total units produced locally in fiscal year 2025. The company's in-house research and development center, boAt Labs, employs more than 100 engineers working across product categories.

Why Does Voice and Vernacular Matter for AI in Emerging Markets?

India represents a unique opportunity for conversational AI because of its linguistic diversity. The country has hundreds of languages and dialects, yet most AI assistants have historically been trained primarily on English. boAt and Google's partnership explicitly targets this gap, aiming to make advanced AI features available to millions of users across India, including those speaking different regional dialects.

"Voice and vernacular are the next frontier for AI in India, and Gemini Live helps make that possible. With Google Cloud's infrastructure underpinning them, boAt can scale this to millions of users across India," said Sashi Sreedharan, Managing Director of Google Cloud India.

Sashi Sreedharan, Managing Director, Google Cloud India

This focus on regional language support reflects a broader shift in AI development. Rather than building one-size-fits-all models, companies are recognizing that conversational AI must adapt to local contexts, cultural references, and linguistic patterns to feel natural and useful to users outside English-speaking markets.

What Capabilities Does Meta's Muse Spark Bring to Multimodal AI?

Meta's Muse Spark represents a different approach to multimodal AI, designed for developers and enterprises rather than consumer devices. The model natively understands and processes various input types including text, images, video, and audio without needing external parsing. This means it can analyze visual charts, interpret documents, or process visual media as part of a conversation.

The model includes a "contemplating mode" that enables multiple sub-agents to reason in parallel, increasing the depth and reliability of outputs for complex or research-heavy queries. This capability elevates Muse Spark's ability to plan and follow through on intensively thought-out workflows. Recent updates such as Muse Spark 1.1 and 1.2 improve the model's ability to handle developer workloads, including coding, tool integration, and longer, more detailed chains of reasoning.

Muse Spark is deployed across Meta's ecosystem, powering Meta AI in the Meta AI app, meta.ai website, smart glasses like Ray-Ban Meta and Oakley Meta, and will be extended to other platforms including Instagram, Facebook, and Messenger. The model is positioned for use by businesses requiring advanced AI for content creation, research, design, or data analysis, as well as developers and tech teams with API access.

How Is Pricing Structured for Enterprise Multimodal AI?

Meta offers two pricing tiers for Muse Spark access through the Meta Model API. The standard tier charges $1.25 per million input tokens and $4.25 per million output tokens, reflecting the full-service option where usage data is not used for future model training. A lower-cost contributor tier allows users to permit their interaction data to be used in model training, with rates approximately $0.10 per million input tokens and $0.20 per million output tokens, with input caching rates as low as $0.002 per million tokens.

These pricing structures reflect the different use cases for multimodal AI. Organizations experimenting with the technology or operating on tight budgets can opt for the contributor tier, while enterprises requiring privacy guarantees and full service can use the standard tier. The significant difference in output token costs suggests that organizations should carefully assess their usage patterns before deployment.

The convergence of consumer-focused conversational AI like boAt's Crest AI and enterprise-grade multimodal models like Meta's Muse Spark signals a broader maturation of audio-visual AI. As these systems become more capable at understanding context, managing multiple tasks in parallel, and adapting to regional languages and cultural nuances, they're moving beyond novelty features toward becoming genuinely useful tools for both everyday consumers and professional workflows.