LiveKit raised $100 million Series C at a $1 billion valuation, led by Index Ventures with Salesforce Ventures, Hanabi Capital, Altimeter, and Redpoint Ventures participating. The open-source-born infrastructure provider powers the real-time voice capabilities behind OpenAI’s ChatGPT voice mode, xAI’s Grok Voice Agent API for Tesla vehicles, and hundreds of enterprise voice AI deployments.

The funding addresses a fundamental infrastructure bottleneck: building production-ready voice AI requires orchestrating speech-to-text, language models, text-to-speech, and real-time communication protocols—a complex technical challenge that most companies struggle to solve in-house. LiveKit abstracts this complexity into a unified platform, turning months of infrastructure development into minutes of deployment.

Deepgram’s $130 million Series C at a $1.3 billion valuation positions the company as the foundational API platform for the emerging B2B Voice AI economy, with over 1,300 organizations already building voice AI functionality on its real-time infrastructure.

Led by AVP with participation from existing investors including Alkeon, Tiger, Wing, and strategic partners like Twilio, ServiceNow, and SAP, the funding accelerates Deepgram’s mission to become the “Stripe of Voice AI”—delivering the critical infrastructure layer that enables billions of simultaneous voice conversations at human-level naturalness and reliability.

Giga’s $61 million Series A led by Redpoint Ventures addresses enterprise voice AI’s deployment speed bottleneck, where traditional implementations take months while Giga delivers production-ready systems in under two weeks.

The San Francisco startup’s success with DoorDash demonstrates how unified real-time orchestration infrastructure can finally make enterprise voice AI practical at scale, moving beyond the pilot purgatory that has trapped 60% of enterprise AI initiatives.

Enterprise Voice AI’s Deployment Bottleneck

Enterprise voice AI has struggled with a fundamental infrastructure problem: deployment complexity that turns promising pilots into months-long integration projects. While chatbots can be deployed quickly, production voice AI requires orchestrating multiple real-time systems—speech recognition, natural language understanding, decision engines, database queries, and response generation—all within sub-second latency requirements.