A video analytics AI agent that can perceive, reason, and act based on massive amounts of video footage must be integrated with existing workflows and applications to be useful. These include content management systems, messaging platforms, databases, ticket queue, and escalation paths.
This integration is challenging because video systems, enterprise knowledge bases, and operational tools are usually siloed. Developers need to capture user intent, retrieve the correct organizational context, generate structured reports, and route findings into downstream systems.
In a previous post, we explained how to enrich video analysis with document knowledge using NVIDIA Blueprints. This post continues with the topic and explains how to unlock the ability to not just analyze video, but to programmatically act on those analyses by introducing NVIDIA NemoClaw. You’ll learn how to:
Extend VSS for guided, context-aware video analysis
Orchestrate the VSS and RAG blueprints as a composable service using NVIDIA NemoClaw
