Why scoring the sender beats reading the message, how a personalized score resists fake accounts, and the research it stands on.
A warm intro is a stranger that people you trust already know. Trust-graph math finds those paths in your own mailbox automatically, so the right strangers still reach you.
VCs and angels get flooded with authenticated cold pitches every day. The few founders worth backing are buried in the noise. Here is how to surface them automatically.
A spam filter blocks obvious junk by reading the message. A trust filter ranks the mail that gets through by scoring the sender. They solve different problems.
Sort your Gmail inbox by your real relationships instead of by time. Kith scores every sender from your own graph and labels mail Kith/Inbox or Kith/Cold, in place.
Trust-graph email reputation rests on two decades of peer-reviewed research: TrustMail, EMIRT, Ostra, EigenTrust, and MeritRank. A short, cited tour of the foundations.
A public reputation score gives every sender one number for everyone. It is gameable and wrong. A personalized score, computed from your own graph, is neither.
AI now writes flawless, fully authenticated cold emails for free. Spam filters cannot stop them, because the message is not the problem. The sender is.
Trust-graph email reputation scores senders by their position in your own relationship graph, using Personalized PageRank over mailbox metadata. Here is how it works.
SPF, DKIM, and DMARC prove an email's domain is not forged. They say nothing about whether you want to hear from the sender. Authentication is identity, not trust.
AI inbox tools read every message and write you summaries. They cut reading time, not volume. To actually fix overload you have to decide what deserves attention first.