Build Anything That Needs a Human with Effect AI

Most software can handle the routine cases well. The hard part is the moment a product reaches something it can't decide on its own: whether a design is clear, whether an answer is right, whether a piece of content should go out. That moment usually needs a person.

Effect AI is built so any app can reach that person on demand. A product sends a task, a qualified human completes it, and the answer comes back as data the product can use. The same building blocks work for a new standalone app or for a human step added to a service that already exists.

Below is how those pieces fit together, a real example of an app built on them, and what else they make possible.

The building blocks

Every human step on Effect is built from the same five parts.

• Templates define what the worker sees. A template is a single HTML file with placeholders like ${image_url} where each task's data goes. It can show images, audio, video, text, or forms, and it sends back an answer in whatever shape you choose.
• Capabilities define who can see it. Workers earn capabilities by passing tests, such as English Language, Music Transcription, or Design Review. Tasks reach only workers who hold the right ones.
• Validation keeps answers usable. Templates check that a response is complete before it can be submitted. Jobs can also send the same item to several independent workers, and self-serve pipelines that pass a labeling job to a separate validation job are coming to the API.
• The Effect API connects it all. Your backend, app, or agent creates jobs, checks their progress, and collects results, the same way it calls any other service.
• Results come back as structured data. Each answer returns paired with the input it came from, along with the worker and the time it was submitted. Worker payments settle on-chain in EFFECT on Solana.

A real example: VibeCheck

VibeCheck is Effect's app in the Canva Apps Marketplace. It lets designers test a design with real people before it goes live, through a Clarity Check, a Clickability Check, or a side-by-side comparison of two versions.

The Canva side is a small interface. Everything else runs on the building blocks above:

1. The design becomes data. VibeCheck exports a static image of the design and pairs it with the creator's purpose, audience, and goal.
2. The data fills a template. Each check is its own template. Clarity asks for a 1 to 10 rating, Clickability flashes the design for a few seconds and asks for a yes or no, and Compare Versions asks for a pick between two designs.
3. Capabilities route the task. Design checks reach only workers who have qualified for design review.
4. People do the part only people can do. A worker reacts to the design and writes down why. The template won't submit until the answer is complete.
5. Results come back to Canva. Ratings, picks, and written reasoning return as structured data, and VibeCheck turns them into feedback inside the editor.

None of these steps were built just for Canva. VibeCheck connects existing Effect features to a place where designers already work.

Two ways to build

The pattern behind VibeCheck isn't tied to Canva. It works anywhere an interface can collect input and call an API.

As a standalone app. VibeCheck lives inside Canva, but the same idea could be its own web or mobile app, a plugin for another design tool, or a browser extension. The app handles the user experience. Effect handles finding qualified people, showing them the task, and returning their answers.

As a step inside an existing service. You don't need to build a new product to add human judgment. A support platform can send low-confidence replies to people for rating. A marketplace can have new listings checked before they publish. A content pipeline can route flagged items to human review. Each one is a single API call at the point where the service needs a person, and the rest of the system keeps running as it does today.

What you could build

If a task can be shown on a screen and answered by a person, it can run on Effect. A few patterns come up again and again.

A person in the loop, on demand. Some steps need human sign-off: approving a payment, confirming a fact, checking that a translation sounds natural, or deciding whether content should be published. Send only those steps to Effect and automate everything else.

People and AI working together. A model drafts and people correct. AI pre-labels images and workers fix what it got wrong. A chatbot flags its low-confidence answers for human rating, and those ratings become training data. Each side handles the part it does best.

Research and data collection. Surveys, preference studies, voice recordings, and labeled datasets all fit the same model, with qualified workers and independent answers built in.

For a closer look at where a person belongs in an AI pipeline, see When Should a Human Step In?

Get Involved

Developers & Researchers: Join API early access for an API key and test credits, then build your first template in the API console.

Organizations: Partner with Effect AI to add human judgment to an existing product or build a new one around it.

Contributors: Sign up for the next alpha phase and earn $EFFECT for real tasks.

Have an idea for an app? Share it on Discord.