Google Opal: Chaining AI Into Real Workflows

Google Opal is one of those experimental tools that immediately clicked for me. It’s essentially a visual pipeline builder for AI — think of it like chaining a bunch of ChatGPT prompts together, where the output of one step feeds into the next. I used it to build a “brand landscape research” workflow: you give it a product name and description, and it chains together input collection, research queries, competitive analysis, and finally generates a full HTML report. I tested it live with a crypto product called “Chain Wallet” and it produced a genuinely useful competitive landscape breakdown.

What makes it interesting

The power here is in the chaining. Any single AI prompt can give you a decent answer, but when you orchestrate multiple steps — where each one builds on the last — you get results that feel much more thorough and structured. Opal lets you view and edit each node’s prompt individually, and you can even pick different AI models for different steps depending on what that step needs to do. Need a creative brainstorm? Use one model. Need structured data extraction? Use another. It also has this neat feature where you can describe a workflow in plain English and it auto-generates the node flow for you, so you don’t have to build the pipeline from scratch.

Who this is for

If you’re a designer or strategist who regularly does competitive research, brand audits, or any kind of structured analysis, this is worth playing with. It’s free right now with a Google account, and honestly the barrier to entry is very low — you don’t need to know how to code, you just need to think about your process as a series of steps. The bigger idea is that we’re moving from “AI as a single conversation” to “AI as an orchestrated system,” and tools like Opal make that accessible to people who aren’t writing Python scripts to chain API calls together.