reddit · SaaS · 11-01 16:50 52 分 ← 返回列表

What I learned building an AI assistant people would trust with their inbox

For the past weeks I've been building an AI assistant that reads my email, Slack, WhatsApp, LinkedIn and calendar and turns them into one to-do list, with the work already prepared: a reply drafted, the lookup done. I use it every day now. Some lessons that surprised me, in case they're useful for anyone building in the AI space: **1. "Drafts, never sends" beat every clever feature.** The first question everyone asked was "what if it sends something wrong?" So it never sends, pays or book

五维评分

付费意愿

需求真实度

个人可落地

竞争空间

新鲜度

AI 分析

痛点：邮件和消息太多，用户需要一个可信的 AI 助手过滤噪音、追踪承诺并准备待办，同时担心隐私泄露和误发。

目标用户：被邮件、Slack、WhatsApp、LinkedIn 和日历淹没的知识工作者、创始人、销售和管理者。

证据：The first question everyone asked was "what if it sends something wrong?" So it never sends, pays or books anything by itself. It prepares, you approve. ... Deciding which 10 out of 300 actually need you is the product. ... For a tool that reads everything, that mattered more to people than I expected.

变现：订阅制个人版/专业版，本地优先隐私作为溢价点；团队版按席位收费；MCP/API 集成和高级承诺追踪作为增值功能。

MVP：2周内做本地桌面端或浏览器插件：接入 Gmail+日历，只读权限，AI 筛选出最需要处理的10条，生成回复草稿并追踪“我答应别人/别人答应我”的事项；所有发送、支付、预订必须人工确认，不做自动执行。

竞品：Superhuman、Shortwave、Google Gemini in Gmail、Microsoft Copilot、Notion Mail、SaneBox

摘要：作者用数周构建了一个跨邮件、Slack、WhatsApp、LinkedIn 和日历的 AI 助手，把消息整理成待办并预备回复。关键经验是“只草稿不发送”、过滤噪音比生成文本更难、追踪承诺最有价值、本地优先解决隐私顾虑、通过 MCP 只读接入其他 AI。帖子显示信任和隐私是真实痛点，但没有付费意愿原话或收入数据，商业化仍需验证。