A Virtual Broadcast Engineer for NVIDIA-Accelerated AI Workflows

AI is becoming core infrastructure for live media. But for most broadcast organizations, the gap between having access to powerful AI models and actually running them in production remains wide. The models are there. The challenge is deploying them efficiently inside live workflows without requiring teams to become experts in GPU infrastructure, networking, and pipeline orchestration.

The swXtch AI Router closes that gap. Acting as a virtual broadcast engineer, the AI Router teaches operators what’s possible, guides them through design decisions, and builds production-ready AI pipelines - all through a simple chat interface. It integrates with NVIDIA NIM microservices and NVIDIA Holoscan for Media to bring GPU-accelerated AI directly into live video and audio workflows.

swXtch AI Router - Platform Architecture

From Models to Pipelines

NVIDIA NIM microservices provide optimized, deployable AI inference services. Integrating those models into live media workflows, however, introduces practical challenges such as connecting live video, managing latency, and orchestrating inference across environments.

The AI Router addresses this directly. Using a natural language prompt, operators can:

• Ingest live video and audio from on-prem, edge, or cloud environments

• Invoke NVIDIA NIM microservices and other GPU-accelerated models

• Combine proprietary and third-party models in a single pipeline

• Apply real-time processing such as active speaker detection, object detection, and upscaling

• Deliver outputs globally in broadcast-ready formats

Pipeline design, deployment, and orchestration are handled automatically via natural language prompts as the operator describes the intent and the platform builds and runs the workflow. For example, using the NVIDIA Active Speaker Detection NIM, the AI Router can demux audio from a live feed, create channel tags for individual speakers, and track each speaker within the frame - all configured through a simple prompt.

An Open AI Marketplace

At the center of the platform is an AI Marketplace which is a curated catalog of video and audio inference models across providers, with off-the-shelf integrations ready to plug into live workflows. Operators can evaluate multiple models, including NVIDIA NIM microservices, within the same pipeline, compare performance in real time, and optimize for latency, accuracy, or cost without reconfiguring infrastructure.

Integrating with NVIDIA AI Technologies

The AI Router integrates with NVIDIA AI technologies to power real-time media processing. NVIDIA NIM microservices run inside the platform, providing access to optimized AI models for analysis, transformation, and enhancement. NVIDIA Holoscan for Media, paired with groundSwXtch, sits at the edge to ingest ST 2110 video and feed it directly into the AI Router, bridging on-premises broadcast infrastructure with cloud-scale AI pipelines. This enables hybrid workflows where live media is processed on-prem, enriched in the cloud, and returned in real time.

This integration allows organizations to leverage NVIDIA’s GPU-accelerated computing within the AI Router’s automated pipeline framework.  swXtch.io is combining the performance of NVIDIA hardware and software with the simplicity of a chat-driven interface.

Removing the Barriers

The primary barrier to AI in live media is not model availability - it’s the complexity of putting those models into production. The AI Router removes that barrier by acting as a virtual broadcast engineer: it teaches operators what AI can do in their workflow, guides them through the options, designs the solution end-to-end, and deploys production-ready pipelines.

Operators don’t need to manage GPU infrastructure, model integration, or pipeline orchestration. They describe what they want, and the platform handles the rest.

See It at NAB 2026

swXtch.io will demonstrate the AI Router with NVIDIA-powered workflows at NAB Show 2026, including active speaker detection, AI-based video upscaling, object detection and metadata generation, and multi-stage pipelines combining multiple AI models.

For a demo, please contact the swXtch team at info@swxtch.io.