Automated applicant pre-screening in Make uses sequential filter modules to reject unqualified candidates before any recruiter reviews them. Sarah, HR Director at a regional healthcare system, built a three-gate Make workflow with dual-branch routing and ATS write-back — cutting time-to-shortlist 60% and recovering six hours of weekly recruiter capacity.

This post drills into the pre-screening implementation layer of an automated HR qualification pipeline. For the broader hiring-process context, see How HR Can Fix Broken Hiring Processes and How a Non-Technical HR Team Started Building Their Own Automations With Make + AI.

Sarah’s Pre-Screening Baseline

Why the ATS Alone Fell Short

Sarah’s team managed hiring across nursing, administrative, and allied health departments — each with distinct qualification requirements. The existing ATS offered basic keyword filtering. Clinical roles demand structured criteria: licensure status, years of direct patient care, shift availability, and geographic proximity to specific campuses. Keyword matching enforces none of those criteria with precision.

The result was a two-stage manual process. Every application was opened and skimmed for baseline disqualifiers. Survivors were then manually moved into department-specific pipelines. Twelve hours per week, before a single interview was scheduled.

The screening bottleneck was the hiring process. Sarah needed filter logic that enforced specific rules — not just matched words.

5 Components of Sarah’s Make Pre-Screening Workflow

1. Structured Intake Form

The workflow begins at intake. Sarah replaced freeform résumé uploads with a structured application form that captured discrete fields: license number and type, years of direct patient care, shift availability (days/evenings/nights/weekends), and home ZIP code. Structured inputs make filter logic reliable. Unstructured résumé text does not.

Every form submission triggers the Make scenario automatically. No data entry required from the recruiting team.

2. Gate 1 — Licensure Verification Filter

The first filter module checks licensure against the minimum requirement for the role type. Applications missing required credentials — or flagged as expired — route to the reject branch immediately. The filter condition is binary: the field either meets the threshold or it doesn’t.

This gate eliminates the largest share of unqualified applicants before any downstream processing runs.

3. Gate 2 — Experience Threshold Filter

The second filter evaluates years of direct patient care against each role’s minimum requirement. Sarah set thresholds by department: three years for charge-eligible nursing positions, one year for administrative clinical roles. Applicants below threshold route to the reject branch. Applicants above it continue to gate three.

Nested filter logic means each gate processes only applications that cleared the previous one — reducing compute and keeping the workflow readable.

4. Gate 3 — Availability and Proximity Filter

The third filter checks two conditions in sequence: shift availability (does the applicant’s stated availability match the open shift?) and geographic proximity (is the home ZIP code within the defined radius of the hiring campus?). Both conditions must pass for an application to reach the advance branch.

An applicant with the right credentials and experience but the wrong shift availability — or who lives two hours from the nearest campus — routes to reject automatically.

5. Dual-Branch Routing and ATS Write-Back

Applications that clear all three gates route to the advance branch: the scenario creates an ATS record, assigns the candidate to the correct department pipeline, and queues a scheduling email to the recruiter. Applications that fail any gate route to the reject branch: the scenario logs the disqualification reason, updates the ATS record, and delivers a courteous candidate rejection — average response time under five minutes.

No recruiter touches the reject queue. The advance queue arrives pre-sorted by department, ready to schedule.

What the Workflow Delivered

• 60% reduction in time-to-shortlist — candidates cleared all three gates within minutes of submission instead of waiting for recruiter bandwidth
• 6 hours/week reclaimed — recruiter time shifted from triage to interviews and offer management
• Automated rejections in under 5 minutes — candidates received timely, professional communication regardless of volume spikes
• Zero additional headcount or software licenses — the workflow ran inside the existing Make plan and ATS
• Department-sorted advance queue — recruiters received pre-routed candidates, eliminating the secondary manual sort

One Discovery Step That Prevents Rework

The workflow Sarah built required two weeks of scoping before a single module was configured. That scoping work — mapping data fields, defining threshold values by department, auditing ATS write permissions — is exactly what an OpsMap™ discovery produces. Teams that skip it build technically correct workflows against the wrong logic. See 7 Questions to Ask Before You Automate Anything for the pre-build checklist.

Related Reading

• How a Non-Technical HR Team Started Building Their Own Automations With Make + AI
• 6 Ways the Make MCP Changes Automation Work for HR Teams
• How Sarah Compressed a 45-Minute Onboarding Process to Under 4 Minutes
• How HR Can Fix Broken Hiring Processes
• The Real Reason Small HR Teams Burn Out: It’s Not the Workload