Skima AI
Home Answer Hub Talent Rediscovery Can we set custom matching criteria for rediscovering candidates?

Can we set custom matching criteria for rediscovering candidates?

September 17, 2026
Akshata Pawar

Akshata Pawar

Senior TA Specialist

About

I’m a senior recruiter with 5 years of experience in talent acquisition, HR, and hiring technology. I write data-driven product reviews, ATS evaluations, and comparisons that help HR leaders choose tools with confidence.

Find Akshata here

Yes, most rediscovery tools let you narrow results using filters like experience level, location, notice period, and salary range, in addition to the core job requirements the matching engine scores against. This lets your team tailor results to a specific hiring situation rather than accepting one fixed output.

Custom criteria typically apply in two places. The job requirements themselves matter first: the more specific your job description, including exact skills, experience range, and location, the more precise the resulting match scores become.

Once results come back ranked, you can narrow the list further by industry, work mode, experience level, notice period, location, and salary range, and you can also limit a scan to candidates created within a specific window, such as the last 6 months or last year, rather than searching your entire history every time.

Tools like Skima AI support all of this. Talent Rediscovery scans your database against a job's specific requirements, then lets you filter the ranked results using the criteria above. You can also scope the Candidate Created Date to widen or narrow the pool depending on whether the role needs someone recently active or benefits from a broader historical search.

One limit worth knowing that most rediscovery tools score matches based on skills, experience, and location relevance rather than letting you manually reweight which factor matters most for a specific search. If a tool you are evaluating claims to let you adjust scoring weights directly, ask for a concrete example of how that changes results before assuming it works as described.