Use case · Staffing & Recruitment
National Staffing Firm: RPA-driven ATS optimisation
An automated workflow that checks candidates’ LinkedIn profiles and syncs new information straight into the ATS, for cleaner data and smarter matching without manual updates.
The challenge
Candidate data that goes stale fast
A large staffing organisation relies on its applicant tracking system (ATS) to match thousands of candidates to open vacancies. But candidate data became outdated quickly: new roles, skills and experience never made it into the system.
That reduced matching accuracy and forced consultants to update profiles by hand, time that should have gone to candidates and clients.
The solution
A self-updating ATS in four steps
The workflow runs automatically and only saves changes that pass validation.
- 1
Detect linked profiles
Find candidates with a LinkedIn URL in the ATS.
- 2
Retrieve updated data
Collect current experience, roles, education and skills.
- 3
Compare
Check the new data against the existing ATS record.
- 4
Update & save
Write validated changes back to the candidate profile.
Core components
What makes it work
Automated profile checking
Candidate profiles are checked for changes automatically, without recruiter effort.
Experience & skills retrieval
The latest roles, education and skills are pulled into a structured format.
Data validation
Every change is validated before it is saved, keeping the ATS clean and trusted.
The impact
Current candidate data, without the manual work
The ATS now keeps candidate profiles current automatically, so recruiters can focus on people instead of data entry.
- Higher data accuracy across the candidate database
- Fewer manual updates for consultants
- Better matching between candidates and vacancies
- Faster sourcing from an up-to-date talent pool
- More effective marketing to candidates
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