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We built an agent that turns messy RFQ emails into priced quotes, and shipped it on Alibaba Cloud

John Ughiovhe 2026年07月21日 02:40 0 次阅读 来源:Dev.to

Every distributor we spoke to has the same quiet bottleneck, and none of them call it a problem. They call it Tuesday. A request for quote lands in a shared inbox. Sometimes it is a tidy bulleted list. More often it is three lines of text from someone's phone, or a PDF that was scanned at an angle. Someone on the sales desk reads it, works out which catalog part each line actually refers to, checks pricing, and types up a quote. A busy desk does this thirty or forty times a day. It is slow, it is boring, and it is exactly the kind of work where a tired person on a Friday afternoon quotes the wrong bolt and nobody notices until the shipment arrives. We spent three weeks building Distill.ai to do that job. This is what we learned, including the parts that went badly. What we actually built You paste an email or upload a PDF. From there a seven stage pipeline runs: parse -> extract -> classify -> match -> price -> policy -> score Parse cleans the document into text. Extract pulls out the individual line items, quantities, and specs. Classify works out what kind of request this is. Match maps each line to a real catalog SKU. Price applies the pricing rules. Policy runs the business checks. Score attaches a confidence value to every match. The interesting part is not the happy path. It is what happens when the model is unsure. Any line that scores below a 0.70 match threshold does not get quoted. It gets flagged with a reason and routed to a human review queue. A person confirms or corrects it, and the quote goes out clean. That one decision is the difference between a demo and something a sales desk would actually put its name on. An agent that is confidently wrong 5% of the time is worse than useless in procurement, because someone has to check all 100% of the output anyway. An agent that says "I got 47 of these 50 lines, here are the 3 I could not resolve" saves real hours. Why Qwen, and how we wired it up We used two models from Alibaba Cloud Model Studio: Qwen-Plus

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