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Automation That Detects Startups with Sudden Hiring Spikes

Given a list of company domains, it scores increases in job postings, ad activity, and technology stacks, then reports them through Slack or a sheet.

n8n

How it works

  1. The creator built an n8n workflow that processes a list of company domains to detect growth signals before funding announcements.
  2. It collects job-posting counts from Greenhouse, Lever, Ashby, and Workable; the duration of ad campaigns from the Google Ads Transparency Center; technology stacks such as PostHog, Segment, and Stripe; and public contact details.
  3. It filters for companies with a growth score of at least 60 or a hiring-intent score of at least 50, then sends open roles and ad counts to Slack, Google Sheets, or a CRM.

Results

The creator explained that a 20-person company posting 8 new openings in 14 days would be a strong growth signal. They did not disclose any companies actually found or the hit rate.

Notes

Signal collection uses a free trial server for hirepulse-mcp, which the creator built, and has a daily limit per IP. They said it could be replaced with other collection nodes. There is no validated evidence that the scoring thresholds are accurate.

Source

Reddit Original post ↗

Example prompt · n8n

Build an n8n automation that finds companies in a watchlist whose hiring and advertising activity has suddenly increased and notifies me.

Inputs: company-domain list to monitor [sheet or direct input], signals to track [change in job-posting count, duration of ad campaigns, analytics and payment tools in use], alert threshold [for example: at least a certain number of new postings within 14 days], notification destinations [Slack, Google Sheets, CRM], run frequency [for example: every Monday]. First, determine how the public job listings on each hiring platform and the Google Ads Transparency Center can be read, and check the applicable terms of use and rate limits.

Implementation steps: 1) Retrieve each company’s open-posting count and roles and compare them with the previous run. 2) Check how long its ads have continued running. 3) Identify tools used on its website. 4) Combine the signals into a score and notify only for companies above the threshold. If data cannot be retrieved for a company, do not score it; add it to a separate failure list.

Deliverables: workflow file, scoring criteria table, and example alert. First run it twice with 5 companies and manually confirm that changes in job-posting counts are calculated correctly.