Workforce & HR · High-volume hiring
Three hundred applications a week and your recruiters are reading the wrong fifty
Banao builds AI that processes every inbound application the day it lands — ranked against the role, with audit reasons on each call — so your recruiters open a shortlist, not a queue.
This is not an off-the-shelf ATS plug-in. It is a purpose-built screening pipeline, and it has been running on Banao's own 300-engineer hiring operation long enough to have sharpened itself on real cohorts before you see it.
Banao— InterviewGod processes every engineering application before any recruiter opens it.
The first call is free · 45 minutes · no obligation
What we build
What a high-volume hiring build includes
Each component addresses one measurable bottleneck in the funnel. We start where the hours go, not where the vendor demo looks impressive.
Application triage at day-zero
Every application parsed, scored against the actual role requirements, and ranked within hours of landing — including night-shift and weekend bursts that would otherwise sit in a queue until Monday.
Structured screen at scale
First-round technical or competency screens conducted at volume with scoring rubrics set by your hiring team, so ten thousand candidates get the same rigour your best interviewer gives ten.
Panel scheduling without the back-and-forth
Availability matching across multi-person panels, time zones, and candidate calendars — candidates receive a confirmed slot without a recruiter manually checking nineteen diaries.
Audit trail on every decision
Every ranking, pass, and reject carries a written reason traceable to the role specification. Recruiters can override any call, and auditors can review any batch — required for regulated industries and DEI commitments.
Funnel analytics by role and source
Where volume drops between application and offer, which sourcing channels actually convert, and how time-to-hire moves week over week — by role family, not one aggregate number.
ATS and HRIS integration
Ranked candidates and screen outcomes write back to your existing applicant tracking system. Your data stays where it lives; the AI sits above your current stack.
Receipts
Already running under load
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified.
Automated first-round screen for every applicant, every cohort
Banao hires continuously for its ~300-engineer bench. Every engineering application goes through a structured InterviewGod screen before any recruiter opens it — scored to a fixed rubric, with the reasoning attached. Recruiters receive a ranked shortlist, not an inbox.
Consistent screening held through high-volume intake periods
Intake peaks that previously overwhelmed a small TA team were handled by the same screening pipeline used day-to-day, with no degradation in scoring consistency between normal and surge periods.
Dogfooding
We hire our own team through this pipeline
Every engineering applicant Banao receives goes through InterviewGod before a recruiter touches the application. The model has run our own high-volume hiring cohorts long enough that we can see where it improves, where it needs override, and how operator corrections sharpen it over time.
That operational history — not a demo environment — is what you get access to when you commission a high-volume screening build. We are not describing a system we specced for someone else. We run it every week.
Screens every Banao engineering applicant before recruiter review.
Runs Banao's own demand-gen pipeline, sourcing the same applicants InterviewGod processes.
The honest version
When high-volume hiring AI doesn't pay back
The case for automation is real at genuine volume. Below that threshold, or with the wrong constraints, it costs more than it saves — and we would rather say that now:
- Low application volume: if you receive fewer than a hundred applications per open role, the manual overhead is small enough that a good recruiter beats any pipeline on speed and cost.
- Employer-brand problem: if applications are low-quality because candidates aren't finding or trusting you, faster triage won't fix it. That is a sourcing and branding problem, not a screening one.
- Governance you can't staff: a high-volume screening model requires someone to own its audit trail, review override patterns, and run periodic bias checks. If there's no one to govern it, we won't ship it.
How we start
How we start — map the funnel before building on it
A platform demo is not a diagnosis. We audit your actual funnel before we quote a build.
- 01
AI Discovery Sprint
2 weeks · fixed price
We sit with your TA team, map where the hours go across your highest-volume roles, and return a prioritised list of automation candidates with ROI maths and a governance checklist — yours to keep. If you proceed, the Sprint cost is credited against the build.
- 02
Build
ATS and calendar integration first, then the screening model trained to your role families and scoring rubrics. Audit trail and recruiter override are built in from the start, not retrofitted.
- 03
Production & continuous improvement
Live rollout with a TA-team onboarding, a hiring funnel dashboard, and a model improvement loop that uses recruiter override signals to sharpen ranking with each cohort.
FAQ
Frequently asked questions
At what application volume does AI screening pay back?
The inflection point depends on your role mix and recruiter cost, but a rough floor is around fifty to a hundred applications per open role per hiring cycle. Below that, a trained recruiter is faster and cheaper. The Discovery Sprint gives you the actual maths for your funnel before you commit to a build.
How do you prevent bias from being amplified at scale?
We train on role-relevant signals only, exclude personal-characteristic fields from ranking inputs, log reasons for every decision, and support batch fairness audits. Every reject is reviewable by a human recruiter, and override patterns are monitored for systematic errors. Governance is a build deliverable, not a bolt-on.
Will candidates know they are being screened by AI?
That is your disclosure decision, and your legal team's. We build the pipeline to support whatever disclosure standard your jurisdiction and policy require — explicit consent flows, candidate-facing explanations, or internal-only documentation. We tell you what we recommend; you decide what to adopt.
Does this replace recruiter judgment on competitive hires?
No. The AI clears the top of the funnel — the hundred applications that should have been ten minutes of work but took two days. Hiring decisions, panel calibration, and competitive offer conversations stay with your team. The point is to give recruiters their hours back for the work that actually requires human judgment.
How does it connect to our existing ATS?
Banao integrates with your applicant tracking system via API or file-based handoff, depending on what your ATS supports. Ranked outputs and screen notes write back to the candidate record in your existing system. Your process does not change; the triage step inside it does.
Get started
Show us your highest-volume role and we'll show you the maths
In 45 minutes we map where recruiter hours go in your current funnel and what automation would return. Bring your noisiest open role — the one your team dreads posting.
Book a Discovery Sprint