Helios is Nucore's data engine. It takes raw, unsorted data and labels and annotates it, turning it into clean training data that helps large language models get better.
The problem
Models learn from labeled data, but most companies only have raw data: documents, transcripts, logs, and images with no labels on them. Labeling it all by hand is slow and costly. Letting a computer label all of it is cheap but messy, and messy data holds a model back.
Our approach
Helios mixes automatic labeling with a little human help, so it gets the best of both.
- Take in raw data. It accepts data of any kind and cleans it up into a common format.
- Label automatically. Models add a first pass of labels and say how confident they are.
- Ask a human only when needed. The easy, confident cases are kept as-is. Only the unsure ones go to a person to check.
- Send it to training. The checked, labeled data is exported and ready to train or fine-tune a model.
Why it matters
By asking humans to review only the hard cases, Helios reaches near hand-labeled quality at a fraction of the effort. And as the model improves, it gets more confident, so even less human review is needed over time.
Takeaway
Good data is what makes models better. Helios turns the raw data a company already has into high-quality training fuel, quickly and affordably.