Data ops · Production

Recruitment platform — 200 production workflows, data integrations 25× faster

The problem

StepStone needed to tie together data from multiple sources — job listings, analytics, CRM — across their recruitment platform. Previously each new data source integration required two weeks of engineering work to connect.

First attempt

Started with individual scripts per integration — fragile, nobody maintained them after the original author left. n8n gave engineering and business teams a shared visual layer they could both work on.

Workflow diagram · grounded in source
1
Data sources
Trigger
2
N8n
Central integration layer
3
Analytics platform
Processing
4
CRM
Output
Reported outcome

200+ mission-critical production workflows.
New data source integrations now take 2 hours instead of 2 weeks — 25× faster. Engineering capacity freed for product work. 'We can speed up the integration of data sources 25×. It takes a maximum of two hours to connect various APIs.' — StepStone tech team.

Reported metrics
Time saved25× faster integration
Volume200 workflows
Running sinceFeb 2025
Reported stack
n8nJob listing APIsCRMAnalytics platform
Source
StepStone case study: 200+ production workflows, 25× faster integrations (via n8n.io case studies)
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

200+ mission-critical production workflows.

What tools did this team use?

n8n, Job listing APIs, CRM, Analytics platform.

What results were reported?

Time saved: 25× faster integration; Volume: 200 workflows; Running since: Feb 2025 (source-reported, not independently verified).

What failed first in this deployment?

Started with individual scripts per integration — fragile, nobody maintained them after the original author left.

How is this data ops AI workflow structured?

Data sources → N8n → Analytics platform → CRM.