Structured Hiring for Recruiters and Engineering Leaders
Stop Guessing.
Start Ranking.
LayersRank helps hiring teams build consistent interview decisions. Structured scoring, reduced bias, and shortlist recommendations you can defend to leadership.
92% Confidence
Avg. decision confidence
Hiring decisions are inconsistent and expensive
Most hiring teams rely on gut feel, unstructured interviews, and score averages that hide critical disagreement. The result: bad hires, wasted pipeline, and leadership that can't trust the shortlist.
68%
of recruiter time spent on candidates who won't pass round one
$240K
average cost of a bad engineering hire
14%
of interview evaluations have significant panel disagreement
Panel disagreement slows decisions, creates extra interview loops, and burns engineering bandwidth. When interviewers score the same candidate differently and there's no way to reconcile, teams default to the loudest voice in the room.
Bad hires delay delivery and reduce team productivity. Offer dropouts disrupt workforce planning after approvals are complete. Leadership loses confidence when shortlist quality cannot be defended clearly.
Hiring Pipeline · Q4 2024
68% wastedPipeline Funnel
163 candidates dropped between screen and technical — recruiter hours lost
Panel Disagreement
Split verdictAverage: 66 — but the 41-point spread tells the real story
How It Works
Three layers. One confident decision.
Structured Interview
Candidate answers role-specific questions via video, text, or MCQ. Every response is captured consistently across all candidates.
Multi-Model Scoring
Multiple AI models evaluate each response independently. When models disagree, the system flags uncertainty instead of hiding it behind an average.
Adaptive Follow-Up
Uncertain scores trigger targeted clarifying questions. The result: every candidate decision arrives with high confidence and a full evidence trail.
The Difference
Not another ATS. A decision engine.
Traditional hiring tools give you scores. LayersRank gives you confidence.
Traditional Tools
- Single score per candidate, no confidence level
- Black-box AI with no explanation
- Same questions regardless of uncertainty
- No way to detect panel disagreement
- Score averages that hide noise
- Reports that don't help leadership decide
LayersRank
- Score + confidence indicator for every dimension
- Explainable evidence trail for each rating
- Adaptive follow-up when confidence is low
- Panel calibration and disagreement detection
- Ranked shortlist weighted by decision confidence
- Audit-ready reports leadership can approve faster
Candidate Report
See what you get
Every candidate receives a detailed report with scores, confidence levels, and evidence trails. No black boxes.
Candidate Report
Priya Sharma
Senior Backend Engineer · GCC Engineering Team
Overall Score
82/100
Confidence
Dimension Scores
Report generated by LayersRank · Multi-model consensus with adaptive follow-up
Enterprise & GCC
Built for GCC scale
Hiring 500+ engineers per quarter across Bangalore, Hyderabad, Pune? You need cross-panel consistency and audit-ready decisions that global HQ can trust.
Pedigree Neutrality
Evaluate candidates on demonstrated ability, not college brand or past employer name.
HQ Approval Support
Generate audit-ready reports that global leadership can review and approve faster.
Offer Dropout Prediction
Identify candidates at risk of declining offers before the approval cycle completes.
Cross-City Consistency
Same evaluation standards across Bangalore, Hyderabad, Pune, and NCR.
Cross-City Hiring Dashboard
Total Open Roles
340
Avg. Consistency
90%
HQ Audit Status
Ready
Bangalore
Backend Engineer
Roles
128
Avg
74
Consistency
Hyderabad
Full-Stack Dev
Roles
96
Avg
71
Consistency
Pune
DevOps Engineer
Roles
64
Avg
73
Consistency
NCR
Data Engineer
Roles
52
Avg
70
Consistency
Powered by LayersRank · Panel scores normalized across locations
How Uncertainty Detection Works
After Adaptive Follow-Up
Score: 63 ± 5 — Confidence restored
The Science
Confidence you can measure
Every score comes with a confidence band. When multiple models agree, confidence is high. When they disagree, the system doesn't average away the uncertainty — it flags it and triggers follow-up questions.
The result: your hiring decisions are backed by measurable evidence, not opaque algorithms. Leadership can see exactly why each candidate was ranked where they are.
Read the Technical WhitepaperReady to rank with confidence?
See how LayersRank evaluates candidates differently. 20-minute demo, no commitment.