Case Studies | Real Automation Projects

Healthcare Recruiting

Automated Candidate Pipeline for Shawn H.

Client: Shawn H., Healthcare Recruiting Agency

Built with:

n8n

Airtable

Indeed

Mailchimp

01. The Problem

Shawn's team was spending 25+ hours per week manually monitoring job boards across 3 platforms (Indeed, LinkedIn, and a niche healthcare board), copying job details into spreadsheets, cross-referencing against their candidate pool, and sending individual outreach emails.

The process was slow, error-prone, and meant qualified candidates were hearing from competitors first. A team of 3 was doing work that should have been automated from day one.

3 team members spending ~8 hours/week each on manual monitoring
Average response time to new postings: 4-6 hours
Frequent missed postings due to human error
No standardized matching criteria, each recruiter had their own method
Candidate outreach was generic, not tailored to specific roles

02. The Solution

We built an integrated system using n8n as the orchestration layer, connecting all three job boards to a centralized Airtable database with automated candidate matching and outreach.

Data Ingestion Layer

n8n workflows polling job boards every 15 minutes, deduplicating listings, and normalizing data into a standard format regardless of source platform.

Matching Engine

Custom scoring algorithm matching job requirements against candidate profiles on 12 criteria: specialization, certifications, location preference, salary range, availability, and more.

Outreach Automation

Personalized email sequences triggered automatically when a match score exceeds threshold. Each email references the specific role, relevant candidate experience, and next steps.

Pipeline Dashboard

Airtable views showing full pipeline: new matches, outreach sent, responses received, interviews scheduled, placements made. Real-time status on every candidate.

03. Technical Details

n8n Airtable Indeed API LinkedIn Recruiter Mailchimp Custom Webhooks

12 n8n workflows handling different parts of the pipeline. Error handling on every node. If a job board API goes down, the system retries 3 times, then alerts via Slack with the failed items queued for reprocessing.

Custom deduplication using fuzzy matching on job titles and company names to prevent the same role from being processed multiple times across platforms.

Rate limiting built in to respect API limits and prevent account flags. Outreach emails are staggered across the day to maintain deliverability.

04. Results

85% reduction in time spent on manual job monitoring and outreach

15 min average response time to new postings (down from 4-6 hours)

3→1 team members needed for the same pipeline volume

40% increase in candidate response rates due to faster, personalized outreach

Real Estate

Lead Generation & Qualification System

Internal project: our own real estate portfolio

Built with:

n8n

VAPI

Airtable

Twilio

01. The Problem

Managing a growing real estate portfolio meant constantly finding new deals. The traditional approach (manually pulling lists, skip tracing, cold calling one by one) was eating 15+ hours per week and barely scratching the surface of available opportunities.

Manual property searches taking 3+ hours/week
Skip tracing done in batches with no automated follow-up
Cold calling limited to ~30 contacts/day
No systematic way to score or prioritize leads
Hot leads sitting untouched while working through cold lists

02. The Solution

Built a fully automated pipeline: property data pull, lead scoring, automated skip tracing, VAPI voice qualification, and CRM routing, all orchestrated through n8n.

Automated List Building

n8n workflow querying property data APIs with our investment criteria. New properties matching the profile get pulled daily and scored against 8 factors.

Skip Tracing & Enrichment

Matched properties automatically sent for skip tracing. Phone numbers and emails appended, then pushed to the qualification queue.

VAPI Voice Qualification

Voice AI calls leads during business hours, asks qualification questions (motivation, timeline, price expectations), and scores responses in real-time.

CRM Routing

Qualified leads auto-routed to our Airtable CRM with full context: property details, owner info, call transcript, qualification score. Hot leads trigger immediate SMS notification.

03. Technical Details

n8n VAPI Property Data API Airtable Twilio Skip Tracing API

8 interconnected n8n workflows handling the full pipeline from property discovery to qualified lead. Each workflow can run independently for debugging and has its own error handling.

VAPI assistant trained on real estate investment conversation patterns. Handles objections, identifies motivation signals, and gracefully ends calls that aren't a fit. All calls recorded and transcribed.

Lead scoring algorithm weights property characteristics (equity, condition, location) and owner signals (absentee, tax delinquent, pre-foreclosure) to prioritize the most likely sellers.

04. Results

200+ leads processed per day (up from ~30 manual calls)

2.3 min average time from new lead to first contact

15 hrs per week saved on manual prospecting

3x increase in qualified pipeline volume

Freelance Automation

Upwork Proposal Automation System

Internal project: our own Upwork freelancing operation

Built with:

n8n

OpenAI

Airtable

Slack

Upwork RSS

01. The Problem

Winning on Upwork requires speed and relevance. The best jobs get 50+ proposals in the first hour. Manually reviewing listings, deciding which to bid on, and writing custom proposals was taking 2+ hours daily, and we were still missing good opportunities.

200+ new relevant job postings per day to review
20+ minutes per proposal to research, write, and customize
Good jobs filled before we could respond
No data on which proposal approaches were working
Inconsistent quality due to proposal fatigue

02. The Solution

An n8n pipeline that monitors Upwork's RSS feeds, scores and filters jobs against our criteria, generates tailored proposals using context-aware AI, and tracks performance metrics.

Job Monitoring

n8n polls Upwork RSS feeds every 5 minutes across our target categories. New jobs are parsed for key details: budget, client history, job description, required skills.

Scoring Engine

Each job scored against 15+ criteria: budget range, client spend history, project type match, keyword relevance, competition level (proposals already submitted).

Proposal Generation

Jobs scoring above threshold get custom proposals generated using AI that references relevant portfolio pieces, addresses specific client needs mentioned in the posting, and maintains our voice/tone.

Performance Tracking

Every proposal tracked: sent, viewed, responded, hired. Data feeds back into the scoring algorithm to improve criteria weighting over time.

03. Technical Details

n8n Upwork RSS OpenAI API Airtable Slack

5 n8n workflows handling monitoring, scoring, generation, submission prep, and analytics. The scoring workflow alone has 23 nodes handling different evaluation criteria.

AI proposal generation uses a carefully crafted prompt chain: first extracts key requirements, then matches against our portfolio, then generates a proposal that addresses each requirement specifically. Not generic templates. Each proposal references the actual job posting.

Feedback loop tracks which proposal styles, lengths, and approaches get the highest response rates. Scoring weights are adjusted monthly based on actual conversion data.

04. Results

200+ jobs scored and filtered daily

<2 min from job posting to proposal ready (down from 20+ min)

2 hrs saved daily on manual prospecting

Data-driven proposal optimization based on actual response rates

Have a Similar Problem?

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