AI-powered real-time speech translation across 120+ languages
Lingopal translates live speech and video into 120+ languages using proprietary AI models that preserve tone and facial nuances. The tech stack—Rust, Go, C++, gRPC, FFmpeg, GStreamer, Kubernetes, and WebRTC—reflects a systems-heavy engineering focus on ultra-low-latency streaming. Active projects center on real-time audio/video pipelines, autoscaling, and audio source separation, while pain points cluster around pipeline latency, streaming reliability, and quality metrics—typical challenges for a company scaling media-infrastructure workloads.
Lingopal operates a real-time speech-to-speech translation platform targeting live streaming, meetings, and video-on-demand use cases. Founded in 2023, the company is headquartered in New York City with 51–200 employees. The product covers 120+ languages with native tone preservation and operates across major platforms including live streams, VOD, phone calls, and meetings. Hiring is accelerating with 6 open roles—5 engineering, 1 sales—skewed toward senior and staff-level engineers, indicating a focus on deepening core infrastructure rather than rapid sales expansion.
Lingopal uses Rust, Python, Go, and C++ for core services; FFmpeg and GStreamer for media processing; Kubernetes and KEDA for orchestration; WebRTC for real-time communication; and AWS for cloud infrastructure. Frontend is built with Next.js, React, and TypeScript.
Active projects include real-time audio/video processing pipelines, low-latency streaming integration, autoscaling strategy design, audio source separation, and ASR/TTS pipeline optimization—alongside speech-outbound sales campaigns and industry event outreach.
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Lingopal's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.