We built a webhook-triggered n8n automation that processes job listings across multiple countries, uses AI to identify companies hiring for targeted roles, and centralizes qualified prospecting data in Airtable. The workflow increased daily leads by 140%, reduced manual prospecting by 90%, and made it possible to scale job-based prospecting across 10× more country configurations.
A multi-country HR tech firm generated leads from public job listings by identifying companies hiring for targeted roles. As the company expanded across the USA, UK, and UAE, each new market required another manual scraping operation, spreadsheet, and review process.
Ciphernutz built a webhook-triggered n8n pipeline that pulls country configurations dynamically from Airtable, processes every region in parallel, uses an AI agent to extract targeted leads from job descriptions, and writes execution and lead data back to a centralized Airtable hub.
"What happened the last time you wanted to add a new country before this was built?"
The last time we added a new country, it was nearly a logistical nightmare, like manually. Every new region meant launching another manual scraping operation, managing new spreadsheets, and accumulating job listings nobody had time to read. Now, we just add the country configuration into Airtable. The system runs everything in parallel, using AI to instantly extract targeted leads. It cuts manual prospecting by 90% while boosting our daily leads by 140%.
Job listings were a valuable prospecting signal because they showed a company had an active hiring need, a budget, and a timeline. The client's domestic process worked reasonably well, but international expansion turned each country into a separate manual operation.
Each country required a separate scraping and research process, with its own searches, filters, spreadsheets, and manual execution. Adding a new market became a project instead of a simple configuration change, making international expansion difficult to scale.
Job listings were often discovered days or weeks after posting, reducing the value of timely outreach. The team also had to manually review job descriptions for relevance, while execution and lead data remained fragmented across regional spreadsheets and ad hoc scraping runs.
High-value prospects were being lost not because the client missed them but because they were being discovered and contacted late.
Someone had to review job descriptions manually to identify relevant prospects, repeating the same judgment call hundreds of times.
We replaced sequential country-by-country operations with a single parallel n8n pipeline that treats geography as configuration. New markets can be added through Airtable without creating separate workflows.
A webhook-triggered gateway starts workflows with parameters supplied by external tools. The system retrieves country configurations from Airtable, dynamically splits country IDs, and processes every configured region in parallel with its own parameters.
An AI lead extraction agent analyzes job descriptions and identifies targeted lead data based on the requested job title. Execution IDs, tracking information, and extracted leads are written back to Airtable, creating a centralized source of truth for downstream prospecting.
The workflow starts on demand from external tools, taking parameters at trigger time. No login, no manual kickoff - other systems can start a run.
Execution IDs, tracking data and extracted leads all write back to Airtable, so there is one place to see what ran, what it found, and what is ready for downstream prospecting. The tracking chaos disappeared because tracking stopped being a separate manual activity.
Replaces the manual searching of job boards. Once triggered, the system programmatically scrapes public job listings across the targeted regions, extracting the raw data, URLs, and descriptions via REST APIs before passing them downstream to the AI agent.
The pipeline runs continuously, which is crucial here: job listings appear across time zones at all hours, and the value of catching one decays by the day. This replaces the old model of monitoring only during one country's business hours, ensuring no high-value prospect is lost to delays.
Daily leads increased 140%, manual prospecting hours fell 90%, and speed to lead improved 95%. Country expansion became a configuration change rather than an engineering project, while continuous monitoring and centralized Airtable tracking gave the team broader coverage and a single view of execution results.
| Before | After | |
|---|---|---|
| Country coverage | One at a time, manual | Simultaneous, parallel |
| Job description review | Human reading, sampled | AI extraction, all listings |
| Adding a region | Manual setup project | New Airtable row |
| Monitoring window | Working hours | 24/7 |
| Execution tracking | Fragmented, ad hoc | Fragmented, ad hoc |
| Daily leads | Baseline | Baseline |
Scale job-based prospecting across countries with automated scraping, AI lead extraction, and centralized tracking.
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