Auto-labeling from a sentence: how Lexi turns prompts into datasets

The interface for building a dataset should be the same one you use to describe the problem to a colleague: a sentence. In LexAnnotate you upload footage and tell Lexi what you care about. Detect the poles, the insulators, and any rust or damaged hardware on the line. That sentence becomes classes, the classes become pre-labels, and the pre-labels land on every frame.

Automation gets you most of the way at machine speed. The last stretch is where trust lives, so every frame passes a human verification step. Verifiers confirm, correct, or reject, and their decisions are logged as decisions. The dataset that comes out is one an expert has actually seen.

Honesty about the ceiling: on objects the wider world photographs constantly - people, vehicles, machinery - the first draft is strong. On the defects that make your domain yours, the hairline weld fault, the early rust bloom, the first pass is weaker, because no foundation model has lived at your site. That is not a reason to label by hand. It is the reason the loop exists.

Every correction a verifier makes teaches the next pre-labeling pass, and on specialized classes a few hundred verified examples close more of the gap than any amount of prompt-polishing. The gap between draft and verdict narrows with each batch, which is why the second dataset on your footage comes together faster than the first. The loop compounds in your favor.

You can open an account from the product itself, with no salesperson in the loop. Upload a clip, write the sentence, and look at what the first draft finds. The product is the demo.