reddit · SaaS · 10-21 11:50 59 分 ← 返回列表

We had 300+ inbound inquiries a month. Here's how I automated lead qualification.

I worked on an automation for a software development agency handling 300+ inbound inquiries every month. The problem wasn't lead generation. It was the time spent manually reviewing inquiries, figuring out which ones needed attention first, and managing follow-ups. I built a workflow using n8n and OpenAI to extract the prospect's needs, budget, and urgency from incoming messages. Instead of letting AI make every decision, I used business rules to qualify leads into A/B/C groups and dete

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AI 分析

痛点：每月300+条询盘的人工筛选、优先级判断和跟进维护极其耗时，导致高价值线索被延误或漏跟。

目标用户：中小型软件外包或B2B服务公司的销售运营负责人、SaaS创始人和增长运营人员。

证据："We had 300+ inbound inquiries a month. Here's how I automated lead qualification." / "The problem wasn't lead generation. It was the time spent manually reviewing inquiries, figuring out which ones needed attention first, and managing follow-ups." / "AI is useful for understanding unstructured messages, but business rules should control important operational decisions."

变现：按月订阅的SaaS（按询盘量或销售坐席阶梯定价）加一次性交付的n8n或Make工作流模板；另外提供收尾的托管运维与调优服务。

MVP：用n8n或轻量自建后端串联：表单或邮箱接入，LLM抽取需求、预算、紧迫度，规则引擎打A、B、C分，自动分派和模板化首响邮件，并生成跟进提醒；前端只做一个线索队列看板（今日必跟、超时未回、高价值未响应），2周内可跑通；先服务3-5家外包公司验证规则。

竞品：Clay、Default、Madkudu、HubSpot Breeze、Intercom Fin、n8n社区模板

摘要：一位运营者给软件外包公司搭了n8n和OpenAI的询盘处理流，客户每月有300+条入站询盘，痛点不是获客，而是人工筛选、判断优先级和跟进的成本。做法是让AI抽取需求、预算、紧迫度，再用业务规则分成A、B、C三档，触发不同路由、邮件与跟进。帖中没有直接付费报价或收入数据，且AI线索评分赛道竞品密集。