Finding the right prospects on LinkedIn can become time-consuming when sales and business-development teams need to identify decision-makers at scale. The process often involves searching for people who match an ideal customer profile, reviewing job titles and company information, checking whether a prospect is relevant, copying profile details into a spreadsheet or CRM, and preparing the records for qualification or outreach.
As lead volume grows, these manual steps can create inconsistent records, duplicate profiles, incomplete lead information, and delays between prospect discovery and follow-up. This use case explores how a connected automation can turn that process into a structured lead-generation workflow by capturing search criteria, sourcing relevant LinkedIn profiles, organising and enriching returned data, removing duplicates, logging clean lead records, and routing batches to the next stage of qualification, research, or outreach.
The Problem
Manual LinkedIn prospecting becomes difficult to manage when sales and business-development teams need to identify decision-makers at volume. Information is often copied between tools, records become inconsistent, duplicate profiles can enter the same sales process more than once, and handing qualified leads off for outreach is slow, repetitive, and error-prone.
The Solution
The LinkedIn Lead Finder Agent receives an ICP description, offer details, and company information. It launches a prospect search through connected sourcing tools, organises the returned data, removes duplicate records, logs clean leads, and routes qualified batches to the next stage of the sales process.
How the Agent Works
- Capture search criteria. The workflow receives the ICP, offer description, company website, and any target-market information through a spreadsheet, webhook, or connected system.
- Run the prospect search. A sourcing tool searches for relevant LinkedIn profiles based on the defined criteria and returns available profile and company information.
- Process and reconnect results. Returned profiles are connected to the original search context so the team can see the audience, offer, and campaign behind each lead.
- Remove duplicates and log leads. The agent compares incoming profile URLs against existing records, filters repeats, and stores clean lead data in a central tracking system.
- Hand off and report. Leads can be passed in batches to a qualification or research workflow, while scheduled reporting summarises lead-generation activity.
Technical Workflow
1. Intake (two entry points)
The workflow can be triggered two ways: a webhook (/lead-finder) for programmatic or API-triggered requests, or a Google Sheets trigger that fires when a new row is added to a “Lead Search” tab. A “Unified Input Contract” code node normalises both sources into one consistent shape, including icpDescription, offerDescription, and companyWebsite, and stamps each request with a unique searchContextId so it can be tracked through the rest of the pipeline.
2. Search agent creation
The normalised request is logged to a Google Sheet (“Copy of Lead Search”), then the workflow calls the Phantombuster API to create a fresh LinkedIn Search Export agent configured with a webhook callback for when results are ready.
3. Launching the scrape
The Phantombuster agent is launched with the ICP description as the search keywords, targeting second and third-degree LinkedIn connections, pulling up to 1,000 results, enriching each lead with additional profile data, and deduplicating profiles automatically.
4. Wait, results and cleanup
The workflow pauses while Phantombuster runs the search, then a webhook delivers the results back into the workflow. A code node parses the raw result payload and re-attaches the searchContextId to each lead. Once processed, the Phantombuster agent is deleted to keep the account tidy.
5. Deduplication
Before writing anything new, the workflow pulls existing profiles already logged in the “Qualified Leads” sheet and filters out any LinkedIn profile URLs that have already been captured, preventing duplicate leads across multiple search runs.
6. Enrichment and logging
Each surviving lead is matched back to its original search context, including the ICP, offer, and company website, and appended to a “Raw Leads” sheet. The workflow stores fields such as name, headline, company, industry, job title, tenure, location, connection degree, and a “Not Yet Sent” connection status flag.
7. Batching and handoff
Leads are grouped into batches of 50 and sent to a separate downstream webhook (/lead-qualifier). Each batch includes run metadata such as run ID, timestamp, batch number, batch size, and total leads, allowing the next qualification or outreach workflow to process the records correctly.
8. Daily metrics reporting
A separate scheduled branch runs nightly at 11 PM. It pulls all raw leads, calculates daily metrics, and uses an OpenAI model to summarise performance into a payload returned through a webhook. This provides a daily summary of lead-generation activity without anyone manually pulling numbers.
Technology and Integrations
Built with: n8n, LinkedIn sourcing tools, Phantombuster, Google Sheets or CRM, webhooks, and OpenAI for metrics summarisation.
Outcome
The LinkedIn Lead Finder Agent creates a more structured and repeatable prospecting process. Teams can move from defined search criteria to clean, enriched lead records without manually managing individual searches, duplicate checks, data entry, or handoffs.
The workflow also gives teams better visibility into where leads came from, which audience or offer they relate to, and how they should move through the next stage of qualification, research, or outreach.
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The LinkedIn Lead Finder Agent is one example of how AI automation can support a more structured prospecting process.
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