What's your workflow for AI-generated prospect research?

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    "summary": "A B2B sales team uses Outreach as the trigger for an automated AI-powered prospect research workflow, which initiates whenever a contact enters a sequence.",
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      "[post] Right now, whenever a contact enters an Outreach sequence, an n8n workflow kicks in.",
      "[post] The finished brief gets added to the Salesforce activity feed before the sequence starts."
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    "post_date": "2026-09-18T11:55:04.000Z",
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    "post_text": "One thing we've noticed is that research is usually the first thing to get skipped once reps start working through bigger lists. We've been trying to automate that step so everyone at least has some context before the first email goes out.\n\nRight now, whenever a contact enters an Outreach sequence, an n8n workflow kicks in. It pulls fresh company data from Coresignal and feeds it into NotebookLM to generate a short research brief. Things like \"the company just opened a London office,\" \"they've been hiring SDRs for the past two months,\" or \"they recently migrated from HubSpot to Salesforce\" tend to make the summaries much more useful than generic firmographics. The finished brief gets added to the Salesforce activity feed before the sequence starts.\n\nThe whole process takes about 90 seconds, which is fine for us. The bigger issue is the summaries themselves. Most are useful, but every now and then NotebookLM latches onto something that's technically true but doesn't really help with personalization.\n\nI’m interested if anyone has tried a similar approach. Are you using a different model for the summaries, changing the prompts, or filtering the data before it gets sent to the LLM?",
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    "post_title": "What's your workflow for AI-generated prospect research?",
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  -d '{"domain":"outreach.ai","limit":20}'