An inbox is a weird dataset — semi-structured, personal, high-stakes for the sender, low-stakes for the recipient. Most “AI email tools” get this wrong by trying to send replies automatically. Ours doesn’t send anything. It triages and drafts, and you stay in the loop.
Labels every incoming email. The agent reads the message, classifies it against your actual Gmail labels (Customer, Vendor, Sales, Personal, etc.), and applies the ones that fit. You stop using manual filters.
Drafts replies where you want them drafted. For emails labeled for auto-draft (customer support inquiries, sales follow-ups, recurring operational questions), the agent generates a reply using your product knowledge base, prior thread history, and a tool call into your CRM if the sender is a known contact. The draft sits in Gmail — you open it, edit if needed, hit send.
Never sends without you. Draft-only, by design. For the 2% of messages that matter most, we don’t want an agent making unilateral decisions. For the 98% that are boilerplate, drafting is where 90% of the time cost was anyway.
Threads and context. The agent sees the full thread before drafting, not just the latest message. It also remembers recent interactions across threads — so “yes, we can do a demo Thursday” works even if the Thursday in question was proposed in a different thread last week.
Under the hood: Gmail MCP server, a triage agent, a drafting agent, and your knowledge base wired in via DataConnect. Runs as a scheduled job (every minute, or on-demand via webhook), with every action logged so you can audit what the agent did on any given morning.
Account executives who burn 90 min/day on follow-ups. Customer support shared inboxes that need everything labeled and half the replies drafted before a human opens them. Founders who want triage but won’t trust auto-send.
Ships in 1-2 weeks for a first team, scales to the rest of the org from there.
Pair with one of our solutions architects. Two weeks from kickoff to a deployed, evaluated, observable agent in your stack.