Distribution
Build trusted professional distribution
The system does the research, prioritization, drafting, and follow-up planning. You approve and publish. Optimized for trusted relationships and relevant reach, never raw connection count.
Execution adapters & safety
Agent run history
Signals & intelligence
Recruiter reached out cold, Michael agreed, and this became a 30-minute interview today (2026-07-17). Example of inbound serendipity to reverse-engineer and reproduce.
captured 21d ago
Open research tasks (2)
Connect: HM for VP, E-Commerce (Toronto)
Peer-led note ready — fill {First}: "Hi {First} — fellow Toronto e-comm operator here; I lead e-commerce & performance marketing at Greenhouse (CPG). Been building our DTC growth + retention engine and would love to connect and compare notes with someone running e-comm at Roots." [needs manual pass — clean current-company HM did not surface via search]
Connect: HM for Director, E-Commerce Operations
Peer-led note ready — fill {First}: "Hi {First} — I run e-commerce & performance marketing at Greenhouse (Toronto CPG). Curious how e-comm ops scales on the grocery side vs. brand DTC. Would love to connect and compare notes." [needs manual pass — clean current-company HM did not surface via search]
Insights
No feedback yet. Rate recommendations to tune future prioritization. Scores stay transparent and editable — the system does not claim to have “learned you”.
Seed from existing app data
Pull strategic categories, a starter set of real people, research tasks for target companies, and content ideas from your existing job-search data. Never fabricates LinkedIn URLs — missing data becomes a research task.