noticed is an AI coworker that helps small teams turn existing relationships into revenue by mapping shared networks and surfacing warm introduction paths. It reads contacts, calendar, and email metadata to brief users daily in Slack, asking for input only when a decision or approval is needed. The product is currently in private beta.
Funding
Funding not disclosed
Founders
Product
Problem
Professionals have thousands of contacts, but their brains can only actively remember about 5% of them. The remaining 95% of relationship history is buried across emails, calendars, messaging apps, and social accounts, making it nearly impossible to leverage one's full network for warm introductions and new business opportunities.
Solution
noticed is an AI coworker that works a user's network in the background to help small teams acquire customers through people they already know. The product maps the user's shared network across their existing tools and works on their stated goals, briefing them daily in Slack with relevant introduction paths and relationship insights. It asks for input only when a decision or approval is needed, and it builds better judgment over time by learning from user feedback and interaction patterns. The system reads only contacts, calendar, and email metadata—never email content or attachments—and cannot send, edit, or delete anything on the user's behalf.
Target Audience
Primary customers are small teams and founders who need to leverage their existing networks for fundraising, sales, and partnership opportunities without manual relationship management.
Features
- Maps shared networks across contacts, calendar, and email metadata to surface warm introduction paths
- Daily Slack briefings that highlight relationship opportunities and ask for input only when a decision or approval is needed
- Goal-based workflow where users share objectives once and the AI works in the background to advance them
- Privacy-first architecture that reads metadata only, never email content or attachments, with no send, edit, or delete capabilities
- Judgment engine that improves over time by learning from user decisions and feedback
- Identity matching using LLMs to handle the long tail of ambiguous contact data without fusing distinct people