AnnotateAI

The core of the platform. You upload raw data and its agents take the first pass at labeling entirely in your browser. For anyone who's spent an afternoon placing boxes by hand, this is the part that changes the math. No more blank canvas. You still review the output, but you're correcting instead of creating.

After the agents finish, you adjust labels, tweak boundaries, and fix false positives. The flow is built around teaching the system what correctness looks like rather than starting from a blank canvas. In practice, this is where most of the actual quality comes from, because AI pre-annotation reliably handles the common cases but tends to miss the unusual ones that decide whether your model performs well outside the test set.

Finished datasets download as YOLO, COCO, VOC, or custom JSON. That covers the formats most training pipelines expect, so you don't need a conversion script sitting between AnnotateAI and your framework. Free exports carry a watermark; Pro removes it, and Pro also unlocks the API access you'd need to trigger exports from your own code.

AnnotateAI processes data through IndexedDB in the browser, so sensitive training data never has to leave your local machine unless you explicitly choose to upload it for hosted processing. For teams handling medical images or licensed content, that removes a big compliance headache. It's a real differentiator against cloud-only annotation services.

Drop in a ZIP and the pipeline handles images, video frames, or document layouts on its own. Why does that matter? Because most labeling tools assume one data type, and switching between them usually means switching tools too. You're not locked into one format before you start. Mixed-format projects are the intended case, not an afterthought.

The Pro plan runs up to 5 concurrent jobs and uses a priority queue that starts about 5x faster. Large datasets are mostly a waiting game, so the queue is what keeps a real production pipeline moving when several jobs are stacked up behind each other. Free accounts sit in a shared community queue with 1 job at a time. That's fine for a test run. It's painful for a deadline.