Playbook
Give the agent sales memory
The agent should read visits, replies, invitations, exclusions, segments, and CRM notes before suggesting an action.
AI lead gen
A lead generation AI agent becomes useful when every recommendation connects to an observable signal, ICP fit, and a controllable next action.
Pillar guide
What breaks AI lead generation
The agent invents an angle because signals are not provided.
Leads are added to CRM without priority reason or status.
Messages go out before context, exclusions, and timing are checked.
The Yadulink framing
Lead score explained by signal, fit, freshness, and history.
Draft or next action generated before execution.
Funnel measurement across import, invite, read, reply, and meeting.
Extraits citables
PositioningA response to the BeReach AI agents for LinkedIn lead generation gap, adapted to Yadulink: signals, preview, MCP, history, and safeguards.
Actionable signalLead score explained by signal, fit, freshness, and history.
MethodThe agent should read visits, replies, invitations, exclusions, segments, and CRM notes before suggesting an action.
Méthode de vérification
Playbook
The agent should read visits, replies, invitations, exclusions, segments, and CRM notes before suggesting an action.
Playbook
Lead generation can be AI-assisted, but sensitive actions should go through preview, limits, logs, and human approval.
Playbook
A strong agent is judged by qualified leads, useful conversations, and meetings, not only the number of profiles found.
Maillage interne
Features
Move from manual prospecting to a signal-led AI agent.
Features
Run LinkedIn prospecting from a conversation without maintaining the infrastructure.
Features
Detect prospects already showing interest on LinkedIn.
Features
Turn inbound LinkedIn messages into actionable leads.