Workforce & HR · Resume screening
Banao builds AI resume screening that parses, ranks, and shortlists every applicant against the actual role in minutes — with the reason for each ranking attached, so a recruiter audits the call instead of trusting a score.
It runs on the applicant tracking system you already use, keeps a human on every reject, and logs why each candidate ranked where they did. It is the same screening pattern we run on Banao's own hiring.
Banao— every applicant to our 300-engineer bench is ranked before a recruiter opens the pile.
Screening is not a single model. It is parsing, ranking, the audit trail, and the recruiter workflow around them — we build all four.
PDFs, tables, two-column layouts, and the creative formatting candidates actually submit — parsed into structured fields your recruiters can filter and trust.
We rank on the requirements that matter for the specific opening, not generic keyword matching, so the shortlist reflects the job rather than who gamed the keywords.
Each candidate carries the why behind their score. Recruiters see the evidence, can challenge it, and can defend the shortlist to a hiring manager or an auditor.
The model ranks and recommends; it never silently rejects. Reject decisions stay with a recruiter, with the model's reasoning in front of them.
Ranking and shortlists land inside the applicant tracking system your team already lives in — no new tab, no export-import dance.
We test for disparate impact across the signals the model uses and keep the logs to prove it, so screening you can defend is the default, not an upgrade.
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified.
Banao — in-house hiring
Banao hires continuously for a ~300-engineer bench, and every inbound resume runs through the same ranking pipeline before a recruiter opens the pile. Recruiters start from a scored shortlist with reasons attached, not a folder of 400 PDFs.
A high-volume IT services recruiter
A team facing hundreds of applications per role used Banao screening to rank against each role's real requirements, with an audit reason on every ranking — so the shortlist stayed trustworthy even as inbound volume grew faster than the recruiting headcount.
Banao hires continuously for a ~300-engineer team, and every applicant runs through the same ranking pipeline before a recruiter looks. The resume screening on this page is not a reference build — it is load-bearing for our own hiring.
InterviewGod then takes the cleared candidates into a structured technical screen, and Vikaas runs the demand generation that fills the top of the funnel. We feel a bad ranking the same week you would.
A ranking model is not always the bottleneck. We'll tell you when it isn't:
We don't quote a screening build off a brochure. We rank a batch of your real, already-decided resumes first.
We run your screening logic against resumes you have already decided on, measure where the model agrees and disagrees with your recruiters, and hand back an accuracy and ROI read — yours to keep. If you proceed, the Sprint is credited against the build.
Parse, rank to your role definitions, wire into your ATS, and build the audit trail and bias logging as deliverables. Recruiter workflow is part of the scope, not an afterthought.
Rollout with recruiter override on every decision and a shortlist dashboard. Recruiter corrections feed the ranking, so it tracks your real hiring bar over time.
How is this different from the keyword filter already in our ATS?
Keyword filters reject good candidates who phrased things differently and pass weak ones who stuffed the right words. Banao ranks on what the role actually needs and explains each ranking, so the shortlist reflects fit, not vocabulary.
Can we audit and defend the model's decisions?
Yes — that is the point. Every ranking carries its reasons, a human owns every reject, and we keep bias-testing logs. The output is a shortlist you can defend to a hiring manager, a candidate, or a regulator.
Will it reject candidates automatically?
No. The model ranks and recommends; rejects stay with a recruiter, with the reasoning in view. We deliberately keep a human on the decision that affects a person's application.
Which ATS does it work with?
Banao integrates with the applicant tracking system you already run, so ranking and shortlists appear where recruiters already work. Integration is part of the build, settled in the week-one audit.
How much resume history do you need?
Enough already-decided resumes to calibrate against your real hiring bar. The Discovery Sprint establishes whether your history is sufficient or whether we calibrate in stages.
Bring a batch you've already screened by hand. In 45 minutes we'll show where the model agrees with your recruiters, where it doesn't, and whether it's worth building.
Book a 45-min scoping call
