GroundX deconstructs the document at a structural level. At the core of this stage is a proprietary vision model trained on over one million pages of real-world documents — not clean training data, but the full spectrum of layouts, formats, and formatting failures that show up in production.

The GroundX vision model reads each page the way a human expert would: identifying layout regions, reading order, table boundaries, headers, footnotes, and visual hierarchies across multi-column formats and mixed layouts that defeat conventional parsers. This is the step most tools skip. It's the reason most tools fail.

Bad data doesn't announce itself. It just quietly wrecks your application downstream — wrong values feeding the wrong decisions, hallucinations dressed up as facts, low-confidence outputs that looked fine until they weren't. GroundX doesn't let that happen.

The Correct stage runs a final validation pass across everything extracted — reconciling conflicting signals, catching ambiguities, and flagging anything that doesn't meet the bar rather than rubber-stamping it through. What reaches your application has been checked, challenged, and cleared.

No silent failures. No garbage dressed up as output. Just clean data you can actually trust.