Original Summary

I spent the last year building a data extraction model targeted to back-office automation developers(emails, invoices, delivery notes, supplier quotes, bills of lading). Instead of using an existing LLM API and creating a wrapper, I trained a custom transformer from scratch with grammar-constrained decoding, grounding checks, and calibrated per-field confidence scores. On paper, the specs are strong: ~350–500ms per document (10–15x faster than standard LLM extraction routes), 0% invalid JSON formatting, and every declared field comes back strictly typed or null It runs on my own hardware in the Netherlands for €0.005 per document. And it beats industry standards such as Textract and DocAI I believed that having better specs than the competition was enough to make developers interested and give my API a try. Instead I noticed an enormous friction and low conversion rate. I sent over a hundred cold outreach emails, messaged with ~5 redditors that have the exact problem I am solving and about 7 developers in this field from my own network. From all of this I received about 10 interested responses. From those responses 2 people gave my product a try. Meanwhile, I watch flashy "System 1 decision" AI models (like Jev) launch with massive hype while being way less accurate and usable for actual production workflows. Because my tool solves a mundane, specific back-office problem, nobody shares it for clout. How did you bridge the gap between a dev saying "Impressive, I'll try it" and actually implementing your product?   submitted by   /u/refractr [link]   [comments]

  • 情报分类：商业与市场研究
  • 分类依据：内容涉及商业、投资或市场动态
  • 信息来源：Reddit · SaaS
  • 发布时间：2026/10/19 07:39:28