A rep needs ten VPs of Sales at fintech companies under 500 people. Right now that means opening Lusha, setting five filters, exporting a list, and copying it somewhere useful. Every search starts from zero.
This play replaces the filters with a sentence. Describe who you need in plain English, and an AI agent turns that into a Lusha search — then hands back verified contacts, ready to use.
Search filters are precise, but precise isn’t fast. A rep mid-conversation in Slack doesn’t want to open a new tab, remember filter names, and build a query. They want to ask for what they need and get it back in seconds.
A working chat interface — Slack, Telegram, or n8n’s own chat window — where typing a request like “find me 10 VPs of RevOps at SaaS companies in the US” returns a list of verified contacts: name, title, company, and whether email and phone are available to reveal.
01
Install the Lusha node and add your API credentials.
02
Add your model provider credentials — Claude or GPT-4o — to the AI Agent node.
03
Connect a chat trigger. Slack or Telegram both work the same way.
04
Set the agent’s system prompt: extract job title, seniority, company size, industry, and location from the request, then call the Lusha search tool.
05
Tell the agent to ask a follow-up question if the request is too vague, instead of guessing.
06
Test it: “Find me 5 VPs of Marketing at mid-market SaaS companies.”
A note on credits: the agent returns names, titles, and companies by default — not revealed emails or phones. Revealing contact details is a separate step, so credit spend stays a decision, not a side effect.
