This video was generated from the original Tygart Media article using NotebookLM’s audio-to-video pipeline — a live demonstration of the exact AI-first workflow we describe in the piece. The article became the script. AI became the production team. Total production cost: $0.
Watch: The $0 Automated Marketing Stack
What This Video Covers
Most businesses assume enterprise-grade marketing automation requires enterprise-grade budgets. This video walks through the exact stack we use at Tygart Media to manage SEO, content production, analytics, and automation across 18 client websites — for under $50/month total.
The video breaks down every layer of the stack:
- The AI Layer — Running open-source LLMs (Mistral 7B) via Ollama on cheap cloud instances for $8/month, handling 60% of tasks that would otherwise require paid API calls. Content summarization, data extraction, classification, and brainstorming — all self-hosted.
- The Data Layer — Free API tiers from DataForSEO (5 calls/day), NewsAPI (100 requests/day), and SerpAPI (100 searches/month) that provide keyword research, trend detection, and SERP analysis at zero recurring cost.
- The Infrastructure Layer — Google Cloud’s free tier delivering 2 million Cloud Run requests/month, 5GB storage, unlimited Cloud Scheduler jobs, and 1TB of BigQuery analysis. Enough to host, automate, log, and analyze everything.
- The WordPress Layer — Self-hosted on GCP with open-source plugins, giving full control over the content management system without per-seat licensing fees.
- The Analytics Layer — Plausible’s free tier for privacy-focused analytics: 50K pageviews/month, clean dashboards, no cookie headaches.
- The Automation Layer — Zapier’s free tier (5 zaps) combined with GitHub Actions for CI/CD, creating a lightweight but functional automation backbone.
The Philosophy Behind $0
This isn’t about being cheap. It’s about being strategic. The video explains the core principle: start with free tiers, prove the workflow works, then upgrade only the components that become bottlenecks. Most businesses pay for tools they don’t fully use. The $0 stack forces you to understand exactly what each layer does before you spend a dollar on it.
The upgrade path is deliberate. When free tier limits get hit — and they will if you’re growing — you know exactly which component to scale because you’ve been running it long enough to understand the ROI. DataForSEO at 5 calls/day becomes DataForSEO at $0.01/call. Ollama on a small instance becomes Claude API for the reasoning-heavy tasks. The architecture doesn’t change. Only the throughput does.
How This Video Was Made
This video is itself a demonstration of the stack’s philosophy. The original article was written as part of our content pipeline. That article URL was fed into Google’s NotebookLM, which analyzed the full text and generated an audio deep-dive. That audio was then converted to video — an AI-produced visual breakdown of AI-produced content, created from AI-optimized infrastructure.
No video editor. No voiceover artist. No production budget. The content itself became the production brief, and AI handled the rest. This is what the $0 stack looks like in practice: the tools create the tools that create the content.
Read the Full Article
The video covers the highlights, but the full article goes deeper — with exact pricing breakdowns, tool-by-tool comparisons, API rate limits, and the specific workflow we use to batch operations for maximum free-tier efficiency. If you’re ready to build your own $0 stack, start there.
Related from Tygart Media
- How We Built a Free AI Agent Army With Ollama and Claude — The deep dive into self-hosted LLMs and how they fit into our production workflow.
- The Fractional CMO Playbook: Serving 12 Clients Without Burnout — How the stack enables one person to manage marketing for a dozen businesses simultaneously.
- The Entrepreneur’s Case for Vertical AI Over Generic Tools — Why purpose-built AI stacks outperform general-purpose tools for specialized businesses.
- I Used a Monte Carlo Simulation to Decide Which AI Tasks to Automate First — How we prioritize which automation to build next using data, not gut feel.
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