Medium ComplexityHR & People OperationsCross-industry2 weeks delivery

HR Recruiting Autopilot

End-to-end hiring pipeline: job post distribution, resume screening via AI, interview scheduling, scorecards, and offer letter generation.

60%

Faster Time-to-Hire

25→4hrs

Admin Hours Per Hire

3x

More Candidates Screened

8

Roles Filled in Q1

Production
1,893 runsLast: 45 min ago
Application received
Make.com

Make.com

Orchestration

Status
Running
TypeTrigger
Executions1,893
node-1
Resume PDF
Claude API

Claude API

AI Screening

Status
Running
TypeAction
Executions1,798
node-2
Score 7+
Calendly

Calendly

Scheduling

Status
Running
TypeAction
Executions1,703
node-3
Interview link
Ashby

Ashby

Tracking

Status
Running
TypeAction
Executions1,609
node-4
All systems operational
4 nodes3 connections

The Problem

The Challenge

A 20-person startup in growth mode needed to hire 8 roles in Q1 but had no dedicated recruiter. The COO was spending 25+ hours per hire on admin: manually posting jobs to 4 platforms, screening 100+ resumes per role, scheduling interviews via back-and-forth emails, collecting interviewer feedback in scattered Google Docs, and generating offer letters from scratch. The process was so painful that hiring managers were avoiding opening new roles even when they desperately needed help.

How We Fixed It

Our Solution

1

Built a Make.com workflow that takes a single job posting in Ashby (their ATS) and syndicates it to LinkedIn Jobs, Indeed, AngelList/Wellfound, and the company careers page simultaneously.

2

Incoming applications flow into Ashby, then trigger a Make.com scenario that sends each resume to Claude API for screening against the role's specific criteria: required skills, experience level, industry relevance, and culture fit signals.

3

Claude returns a structured score (1-10) with reasoning for each criterion, plus a go/no-go recommendation. Candidates scoring 7+ automatically advance to the next stage in Ashby.

4

Qualified candidates auto-receive a personalized email with a Calendly link for their first-round interview, with automatic timezone detection and interviewer availability sync.

5

After each interview, the system generates a pre-filled scorecard in Notion from the interviewer's calendar notes, with standardized rating criteria and space for qualitative feedback.

6

When a candidate is marked 'Offer' in Ashby, a Google Docs offer letter auto-generates from a template with the candidate's details, compensation, start date, and role-specific terms.

Tools & Infrastructure

Tech Stack

M

Make.com

Workflow orchestration. Manages the multi-platform job distribution, resume routing, candidate communication, and offer generation pipeline.

C

Claude API

AI resume screening. Evaluates candidates against role-specific criteria with structured scoring, reasoning, and go/no-go recommendations.

C

Calendly

Interview scheduling. Provides one-click booking links with automatic timezone handling and interviewer calendar sync.

A

Ashby

Applicant Tracking System. Central hub for candidates, pipeline stages, and hiring team collaboration with ATS-grade reporting.

Impact & Outcomes

The Results

60%

Faster Time-to-Hire

Average time-to-hire dropped from 35 days to 14 days by eliminating bottlenecks at every stage.

25→4hrs

Admin Hours Per Hire

Administrative time per hire dropped from 25+ hours to just 4 hours — an 84% reduction.

3x

More Candidates Screened

AI screening processes 3x more applicants than manual review, ensuring no strong candidates are missed.

8

Roles Filled in Q1

Successfully filled all 8 planned roles within Q1, hitting the hiring target for the first time.

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