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Case Study

From 22 to 31 Placements/Month — Same Team

How 6 AI agents boosted placements by 41%, cut admin time by 76%, and reduced time-to-fill by 14 days for an 18-consultant staffing agency.

41%
More Placements
76%
Admin Time Cut
6
AI Agents
14 days
Faster Time-to-Fill

The Challenge

A respected mid-market recruiter in DACH, placing IT and engineering professionals. But their 18 consultants spent 70% of their time on non-revenue activities — sourcing, screening, writing reports, and chasing follow-ups — leaving little time for the relationship work that drives placements.

The Sourcing Time Sink

22 hrs/week manual LinkedIn searching, 60% of sourced candidates didn't match.

The Follow-Up Black Hole

340 active candidates, 45% going cold before second touchpoint.

Client Reporting Drag

14 clients × 4 hrs = 56 hrs/week on progress reports.

Knowledge Loss on Departure

When consultants left, all relationships and market knowledge walked out.

The Solution

AI recruitment orchestration where consultants request candidate searches, client reports, and market research via email or WhatsApp — receiving polished results from 6 specialized agents.

Candidate Discovery Agent

AI sourcing across LinkedIn, Apollo.io, and 12K-candidate database with 15-criteria scoring.

Candidate Engagement Agent

Hyper-personalized outreach sequences with multi-step follow-up and optimal timing.

Client Report Agent

Auto-generates weekly client progress reports with pipeline status and candidate profiles.

Knowledge Vault Agent

Institutional memory capturing all interactions, notes, and placement history.

request → search → screen → shortlist IDLE
1 — A consultant asks for a search
requested by WhatsApp between client calls
Request: who have we got for the Basel controlling role?
From: one of 18 consultants
Against: the firm own database, first
Brief: seniority, sector, language, notice period
2 — What lands back in the inbox
01Candidates screened against the briefranked
02Placements per month, before vs after22 → 31
03Time-to-fill-14 days
A shortlist with its reasoning attached
Representative example. The workflow is real; the specimen document is synthetic — client files never leave the client.

System Overview

AI recruitment pipeline from sourcing through scoring, matching, and placement — processing 2,400 candidates down to 94 placements per quarter.

Sourcing channels, candidate CVs, and the ATS converge on one orchestration layer HIRING SYSTEMS AGENTS Sourcing channels job boards, referrals Candidate CVs 2,400 per quarter ATS pipeline of record Orchestration layer routes each candidate Scoring against the role Matching rank, shortlist Placement tracked to hire ORCHESTRATION

Implementation Timeline

Phase 1

ATS & Data Integration

Week 1

Integrated Bullhorn ATS, LinkedIn, and Apollo.io data sources.

Phase 2

Agent Development

Weeks 2-3

Built candidate discovery, engagement, reporting, and knowledge vault agents.

Phase 3

Outreach Engine

Week 4

Developed personalized outreach sequences with A/B testing and timing optimization.

Phase 4

Pilot & Calibration

Weeks 5-6

Piloted with 6 consultants, calibrated scoring models, rolled out to full team.

The Results

Placements increased from 22 to 31 per month (41% growth) with the same 18-person team.

Time-to-fill reduced from 38 to 24 days — 14 days faster from job order to placement.

76% reduction in admin time — consultants now spend 85% of day on revenue-generating activities.

Candidate response rate increased from 12% to 34% through AI-personalized outreach.

Client reporting time eliminated — 56 hrs/week to zero, auto-reports rated 4.8/5 by clients.

Time Savings

Hours per week — before and after AI orchestration deployment across all recruitment operations.

Recruitment Time Savings Before vs After

"Our recruiters used to be expensive administrators who occasionally placed someone. Now they're full-time closers. We went from 22 to 31 placements per month without hiring a single new consultant."

Managing DirectorIT and engineering staffing agency, Name withheld at the client's request

Technologies Used

n8nGPT-4oClaude APIBullhorn ATSLinkedIn APIApollo.ioWhatsApp Business APIPostgreSQLpgvector

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