I spend two to three hours every day reading about new AI tools. It’s part of the job. When you’ve been doing SEO (Search Engine Optimization) for nearly thirty years, you develop a sense for what’s actually useful versus what’s just noise wrapped in a shiny interface.
Most tools fall into the noise category. They solve problems you don’t have, create more friction than they eliminate, or are essentially ChatGPT with a different skin and a monthly subscription fee.
But every once in a while, something genuinely changes how we work.
Google Opal is one such tool.
It’s still experimental – tucked away in Google Labs, invite-only access – but Harold de Guzman and I have been testing it for the past few weeks on our actual content production workflow. And I wanted to share what we’re seeing because this has real implications for anyone creating regular content.
The Problem Opal Solves
Let me paint the picture. I record a podcast interview, Better Data Builds Better AI with Ananya from Microsoft, talking about RAG architecture, vector search, and practical AI implementation.
The recording itself takes an hour, including setup. Straightforward.
Then the post-production begins. And this is where most content strategies fall apart.
That single podcast needs to become: a blog post summarizing the key points. Multiple social media posts for LinkedIn, Facebook, and Twitter. YouTube upload with proper thumbnail, title, description. Email to our newsletter list. Video clips highlighting specific moments. Graphics for promotion.
Traditionally, that’s a full day of work across multiple tools. You’re in a word processor for the blog. Canva for graphics. Video editor for clips. Email platform for the campaign. Each tool has its own interface, learning curve, and friction.
Harold manages this workflow for Boulder SEO Marketing. He’s good at it. But even being good at it means hours of work per piece of source content.
Opal consolidates all of that into one platform. And it does it through natural language – you describe what you want, and it builds the workflow to deliver it.
How Opal Actually Works
The concept is “AI mini-apps.” You prompt Opal with what you’re trying to accomplish, and it constructs a series of processing steps – they call them cards – that work together to produce your output.
Harold’s prompt was direct: generate content from a transcript. Blog post, social media posts, YouTube thumbnail, email campaign.
Opal understood the intent and automated the entire workflow. Upload a transcript, hit enter, and wait two to three minutes while it processes through each card.
The output from our most recent test:
A complete blog post that accurately captured the podcast conversation. Not a generic AI summary – actual representation of what we discussed, with correct names and concepts.
YouTube thumbnails are generated from a combination of a base photo and transcript context. The AI pulled relevant topics directly from the conversation and incorporated them into the thumbnail design.
Four to five social media image variations, each with different visual approaches.
Social media post copy ready to publish.
An email campaign written in my voice – and it actually sounded like something I’d send, not generic marketing copy.
Even a video summary clip with audio.
One transcript input. Ten pieces of content output. Two to three minutes of processing time.
The Quality Reality Check
Look, I’ve tested enough AI writing tools to be skeptical. They hallucinate. They make up facts. They attribute quotes to the wrong people. They sound confident even though they are entirely incorrect.
The first thing I checked in Opal’s output was accuracy.
The podcast we tested was technical – conversation with Ananya from Microsoft about RAG (retrieval-augmented generation), vector search, and building AI systems without training custom models. Specific terminology, proper names, nuanced concepts.
The blog post got it right. Names were correct. Concepts were accurately represented. The content reflected what we actually discussed, not some AI-generated approximation that sounded plausible but missed the point.
Is it perfect? No. Human review is still required. The garbage-in, garbage-out principle applies: better source content and better prompting produce better output. But as a starting point that gets you 80% of the way there? This is significantly more reliable than most tools I’ve tested.
The Workflow Architecture
Let me get specific about what Harold built.
The primary workflow handles transcript-to-content transformation. You upload the transcript file, and Opal processes it through sequential cards:
Card one: Blog post generation. The AI analyzes the transcript, identifies key themes and talking points, and produces a structured blog post.
Card two: YouTube thumbnail creation. Combines a base image with transcript context to generate relevant thumbnail designs.
Card three: YouTube description. Formatted properly with timestamps, links, and summary content.
Card four: Video summary clip. Creates a short video with audio summarizing the content – no separate video editing required.
Card five: Social media images. Multiple variations for different platforms and angles.
Card six: Social media post copy. Ready-to-publish text for various platforms.
Card seven: Email campaign. Written in the specified voice and tone.
Harold also built a secondary workflow specifically for YouTube thumbnails. Upload a base photo plus a transcript, prompt for five thumbnail variations, and Opal generates options with text pulled directly from conversation topics.
One of our thumbnails came out with “Bad Data, RAG, and Lock Answers Without Training Your Own Model” – exact topic coverage from the podcast, automatically incorporated into the design.
Why This Matters for AI Search
Here’s the connection to GEO – Generative Engine Optimization – that I talk about constantly.
Google’s AI Overviews, ChatGPT, and Perplexity all evaluate content comprehensiveness when deciding what to cite. Pages with supporting multimedia, multiple content formats, and thorough coverage signal that someone invested real effort.
When you can efficiently transform one piece of source content into blog posts, videos, social media, and email campaigns, you’re creating a content ecosystem around each topic. That ecosystem sends stronger signals of authority than a single blog post in isolation.
The businesses that will dominate AI-driven search aren’t necessarily creating more content. They’re producing more comprehensive content coverage from fewer sources. Opal enables that strategy at scale.
How to Access Opal
Opal is currently in Google Labs at labs.google. You’ll need to sign up for access – it’s not publicly available yet.
If you’re already in the Google ecosystem and producing regular content – podcasts, webinars, video series – this is worth getting on the waitlist. The workflow efficiency gains are substantial.
The Broader Pattern
This is what I keep seeing in the AI tool landscape: the valuable tools aren’t the ones that replace human work entirely. They’re the ones that eliminate the friction between creation and distribution.
The podcast interview still requires human expertise. The strategic decisions about what topics to cover, who to interview, what angles to explore – that’s human work.
But the mechanical transformation of that interview into multiple content formats? That’s precisely what AI should be handling. It’s repetitive, time-consuming, and doesn’t require creative judgment.
Opal fits that pattern perfectly. It doesn’t replace the thinking. It eliminates the busywork.
What’s Next
Harold and I will continue testing tools like this and reporting on what works. The AI landscape is noisy – new tools launching daily, most of them not worth your time.
Our filter is simple: does this solve a real problem we actually have? Does it integrate into existing workflows without creating new friction? Is the output quality sufficient to serve as a starting point?
Opal passes all three tests.
You can attend our AI SEO & GEO Online Summit to go deeper into AI SEO and GEO strategy, covering what’s actually working right now in generative search optimization. Links are below.
The reality is this: content creation isn’t getting easier. The bar keeps rising. But the tools for meeting that bar are improving rapidly. Opal is evidence of that.
Stay safe and healthy. I’ll see you next week.
Cheers,
Chris