Workforce & HR · Resume screening
Your best candidate is somewhere in the 400 resumes nobody read
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.
The first call is free · 45 minutes · no obligation
What we build
What a Banao screening deployment includes
Screening is not a single model. It is parsing, ranking, the audit trail, and the recruiter workflow around them — we build all four.
Parsing that survives real resumes
PDFs, tables, two-column layouts, and the creative formatting candidates actually submit — parsed into structured fields your recruiters can filter and trust.
Ranking against the real role
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.
A reason on every ranking
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.
A human on every reject
The model ranks and recommends; it never silently rejects. Reject decisions stay with a recruiter, with the model's reasoning in front of them.
ATS-native, not a second system
Ranking and shortlists land inside the applicant tracking system your team already lives in — no new tab, no export-import dance.
Bias auditing built in
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.
Receipts
Where this runs
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified.
Every applicant ranked before a recruiter reads one
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.
Shortlist quality held while volume climbed
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.
Dogfooding
We screen our own applicants on this exact pipeline
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.
Picks up the candidates our screening clears for a structured technical interview.
Runs Banao's own demand-gen pipeline end to end.
The honest version
When resume screening isn't worth automating
A ranking model is not always the bottleneck. We'll tell you when it isn't:
- Thin applicant pools: if a role gets twenty applicants, a recruiter reads them all faster than you can train and govern a model. We'll say so.
- Roles judged on portfolio, not resume: design and some senior work is judged on samples a resume can't carry. Screening helps less, and we won't pretend otherwise.
- No clean role definition: if nobody can say what good looks like for the role, the model has nothing honest to rank against. That conversation comes before any build.
How we start
How we start — prove it on your own resumes
We don't quote a screening build off a brochure. We rank a batch of your real, already-decided resumes first.
- 01
AI Discovery Sprint
2 weeks · fixed price
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.
- 02
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.
- 03
Production & continuous learning
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.
FAQ
Frequently asked questions
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.
Get started
Put a stack of your real resumes in front of it
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 Discovery Sprint