AI Resume Screener | Sistava HR Team
Skill match, level fit, and red flags surfaced in seconds.
The HR Team is a team of AI Employees on Sistava.
Screening is the step that quietly decides how long a hire takes, because two hundred applications is a job nobody has a spare day for. We read every one against the role, score it on the criteria you set, and hand back a ranked list with the reasoning attached to each name.
You spend your reading time on the strongest twenty rather than on the first twenty that arrived. Time to first interview drops because the bottleneck was never the interviewing.
The scoring is only as good as the criteria, which is why the criteria are yours and visible. You say what the role genuinely requires, what is merely nice to have, and what should carry no weight at all. It scores against exactly that and shows why each candidate landed where they did, so when you disagree you are correcting a written rule rather than arguing with a black box.
It reads more than the PDF. Where a candidate links a portfolio, a repository, or published work, it can go and look, which matters most for the people whose actual evidence was never going to fit on a résumé. Gaps get noted as facts rather than as verdicts, because a two-year gap has a hundred explanations and none of them belong to a scoring rule.
The one thing it must not do is decide. Every candidate stays on the list with their score and reasoning, so a decision to pass on someone is one you made and can revisit. Automatic rejection is not the design, and treating a ranking as a rejection is the fastest way to lose the candidate who did not describe themselves the way your rubric expected.
What you get
- Every application read, not just the first ones in the pile
- Scored against criteria you wrote and can change in one sentence
- Reasoning shown per candidate so you can disagree with it specifically
- Portfolios, repositories, and published work followed where linked
- Nobody removed from the list, only ranked within it
- The screening bar improves as you correct it, and the corrections persist
How it works
- Agree the bar: Must-haves, nice-to-haves, and explicitly what should carry no weight.
- Read everything: Every application, plus the portfolio or repository where one is linked.
- Score with reasoning: A rating per criterion and the evidence behind it, not a single opaque number.
- Rank and hand over: A ordered list where the strongest candidates are at the top and nobody has been deleted.
- Take the correction: You disagree once, the bar is updated, and every later batch is screened the new way.
FAQ
Does it reject candidates?
No. It ranks and explains, and you decide who advances. Keeping every candidate visible is deliberate, because the ranking is a reading order rather than a verdict.
How do I stop it weighting something I do not care about?
Tell it. School names, employer prestige, and formatting are common ones to strike out, and the change applies from the next batch onward rather than needing a new configuration.
What about non-standard applications?
A portfolio site, a repository, a case study, or a video link all get read where they are provided. Those candidates are usually the ones a keyword filter loses.
How fast is it on a big pile?
A backlog of a few hundred is a single job rather than a week. The useful consequence is that late applications get the same attention as early ones.
Can I see why someone scored low?
Yes, per criterion. That is the check that matters, because a score you cannot interrogate is a score you should not act on.