How I Built Two AI Mini-Apps With Google Opal

Google Opal has quickly become one of my favourite tools for building AI workflows without touching a single line of code. As someone who lives between SEO, content creation, and automation, I wanted a faster way to turn ideas into usable tools instead of one-off prompts. developers.googleblog

What Is Google Opal?
Google Opal is a no-code AI builder from Google Labs that lets you describe an app in natural language and then turns it into a visual mini-app. You get a canvas with inputs, AI generation steps, and outputs that you can rearrange and edit without traditional development. developers.google

The Problems I Wanted To Solve
In my day-to-day work, I kept repeating similar tasks:
Turning keyword lists into content ideas and outlines.

Repurposing long-form content into social posts.
Keeping messaging consistent across multiple platforms.
Doing this manually was slow and easy to mess up when handling multiple brands at once. I realised I did not just need “better prompts”; I needed reusable systems.

Opal 1: SEO Content + Outline Helper
The first Opal I built focuses on SEO content ideation and structure. I start with a topic or keyword, and the app:
Asks for a bit of context (audience, intent, platform).
Generates a clean blog outline with headings and subheadings.
Suggests angle variations and title options I can test on dev.to or my own blog.

This turned my messy ideation process into a consistent, repeatable flow that I can run for any niche.
Opal 2: Content Repurposing + Social Helper
The second Opal is built for repurposing. I paste in a blog draft or long-form piece and the app:
Extracts key points and hooks.

Creates multiple social media snippets tailored for platforms like LinkedIn and Instagram.

Suggests CTAs and variations I can A/B test in posts and captions.
Instead of writing every caption from scratch, I now have a structured “repurposing engine” working on top of my long-form content.
Building These Flows Inside Opal
In both apps, I used Opal’s natural-language description to generate the initial workflow. Then I iterated manually in the visual editor: renaming nodes, tightening prompts, and adding guardrails so the outputs are more predictable.
A typical flow looks like:
Input node for topic or content.
One or more Gemini-powered steps for analysis and generation.
Output steps for organized text I can copy into docs, CMS, or social schedulers.

Lessons Learned From These Mini-Apps
Natural language is a great starting point, but real reliability comes from editing each node with specific instructions and constraints.

Thinking in workflows (inputs → transformations → outputs) beats trying to do everything in one giant prompt.

For marketers and indie devs, Opal is an excellent way to prototype ideas before you invest in a full custom-coded solution.

If you are curious, you can take the same idea and build your own: one Opal for SEO content, one for repurposing, and then keep evolving them as your content strategy grows.