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This sample demonstrates how I qualify B2B prospects against specific criteria, verify public-source information, add source links, and clearly flag anything that needs more research. The final deliverable is a structured Excel or Google Sheets file ready for outreach.
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A production-ready LangGraph agent that discovers, audits, scores, enriches, and qualifies B2B prospects automatically.
Adding new sections to my portfolio, worked on a new trust section to show the different B2B founders and teams I've worked with.
Beautiful project.
I built an AI-powered n8n automation that generates personalized ice breakers for cold emails.
It takes a company’s website URL, analyzes the business, extracts key information, and uses AI to create a unique opening line for outreach.
Instead of manually researching every prospect, this workflow helps personalize cold emails at scale while saving hours of repetitive work.
Built with n8n + AI + web data extraction.
Strong use case. The quality control matters as much as the generation: I’d keep the source snippet and URL beside every ice breaker, then use a fallback when the evidence is thin instead of letting the model infer. Have you added a similarity check so repeat prospects don’t receive nearly identical openings?