Clean finds companies that already have a reason to buy what a team sells, then names the person to contact and shows the dated records behind the timing. The seller starts by sharing a website and documents so Clean can learn the product and draft an ideal customer profile. From there it matches that profile to activity such as equipment loans, building permits, customs shipments, contract awards, and supplier changes, and puts those events in order until they add up to a reason to reach out. The product is aimed at B2B teams selling into manufacturing plants, employers and insurance firms, and law firms or in-house legal teams. What comes back is a short list of profiles. Each one carries the reason in plain language, the source records, a contact window, a decision maker with verified email and phone, and any introduction path through the seller's network. Nothing sends until the user turns a campaign on. Clean can draft the message and send it from Gmail, capped at 40 emails a day per mailbox, with the sequence stopping on reply, or hand the account to HubSpot, Attio, LinkedIn, and other tools already in the stack. The company describes this as research from what businesses actually do, set apart from contact databases and from intent data built on ad clicks and page views. Clean AI Labs, the company behind the product, is in Y Combinator's Fall 2026 batch and is also backed by Jackson Square Ventures. Founders Pavan Kumar, Pratham Patel, Clarissa Saputra, and Tejas Gupta are building it. Pavan Kumar posted the launch with a manufacturing example in which a loan, a permit, and incoming equipment are read together as a short window to call, which is the use case the team is taking to sales organizations and to builders of sales agents.