Self-Hosted AI Video Architecture Advisor

I’m building a local, upgrade-friendly AI video workflow conceptually inspired by AutoClips V3, with the full stack models, orchestration, and interface running on-premises in Docker. AutoClips V3 demonstrates the kind of end-to-end workflow I want to emulate conceptually, from topic and script generation to scenes, captions, preview, rendering, and channel-ready output. I will handle much of the implementation through a local AI-first workflow, but I need an experienced technical advisor to define the system architecture, select the right repositories and containers, and step in when either the models or I reach a blocker. Your main responsibility is to help design a stable, low-error, maintainable system. That includes guiding the infrastructure setup, defining clean module boundaries, and ensuring the stack remains easy to update, reproduce, and deploy on machines with similar specifications. Modular architecture is commonly used to improve maintainability and scalability by separating systems into clearer, more manageable components. I’m looking for support in the following areas: High-level architecture design and rationale Docker-based deployment planning, including Compose and, if justified, Kubernetes manifests Recommendations on model selection, optimization strategies, and integration patterns that keep latency reasonable Troubleshooting when integration issues appear and the AI-assisted workflow cannot resolve them Guidance on reproducible deployment, versioned configuration, and upgrade paths across similar machines The target outcome is a system that is: reliable in daily use easy to redeploy easy to upgrade without breaking the core stack simple to operate on compatible local machines Acceptance criteria: The full stack starts locally from a single documented command, with all core services reporting healthy status. Health checks are a standard best practice for confirming that containers are actually ready, not just running. End-to-end video tasks complete successfully with no critical errors across three consecutive test runs. Clear upgrade documentation allows models and key components to be replaced, updated, or retrained without rebuilding the entire system. Reproducible, containerized environments are widely used to support this kind of maintainable upgrade path. If your strength is turning ambitious self-hosted ideas into dependable, modular systems, I’d be interested in discussing the project further.

Project ID: 40399341