Most intent data is keyed to a company, a topic and a window of time. It shows what a company reads online. It doesn't show that a plant just won its first aerospace customer. Clean starts from a specific change at one plant instead, researching manufacturers plant by plant: the site where the work happens, not only headquarters.
An audit date on its own says less than it seems to. In Clean's database every plant's history is a dated timeline, and a temporal graph network runs over it, because order and spacing carry more than any single event: a new aerospace customer, then a first quality manager hire, then an audit date tells a very different story from the audit date alone. Clean's catalogue of early signs passes 4,000 across the 14 buying moments, and quality is one of its deepest parts. On Clean's own scoring, about 7 in 10 of those signs are specific enough to identify the machine, program or deadline at stake.
A quality seller gets each reason with its date and evidence attached, next to whatever else could explain the same facts and the finding that would prove it wrong. Where Clean cannot confirm something, it says unknown instead of guessing. Before any of it reaches you, Clean has already dropped contractors, repair and service shops and one-person operations, the look-alikes that pass for plants on paper. Clean doesn't send anything either: it names the plant and the reason, and your team makes the contact. Book a demo and the call ends with a live list of plants built for your quality product, or read how Clean works if you'd rather start there.