Λ-CORE·aperture's scoring model
AI candidate scoring, from resume to shortlist.
Most hiring tools tell you what happened. Λ-CORE tells you what it means. structured behavioral interviews, six-dimension scoring with confidence ranges, and pool-relative rankings that update as evidence accumulates.
Resume parsed
How λ-CORE builds questions from a resume.
A resume tells us where someone has been. aperture reads every application for skills, experience depth, and role fit, then uses that baseline to calibrate every question that follows. Stronger signals lead to deeper probes. Thinner ones surface the fundamentals.
Baseline established
Why every candidate gets a different interview.
The resume analysis sets a match score weighted against what the role actually requires. A strong background unlocks deeper questions. A thin one gets more foundational probes. Λ-CORE knows the difference before the conversation begins.
Behavioral interview
A structured interview that adapts, not a form.
A 15-minute behavioral interview conducted by aperture for every candidate, without scheduling. Questions evolve based on what each person actually says. No interviewer fatigue, no inconsistency, no unconscious bias. Just a clean read on how someone thinks.
Λ-CORE evaluation
Candidate scores with a confidence range.
Λ-CORE scores six behavioral dimensions on a 10-point BARS scale. Each score comes with a confidence interval, because a single number without context is just a guess. Narrow interval means high confidence. Wide interval means ask more questions.
Pool-relative ranking
How candidates are ranked against the whole cohort.
Scores don't exist in isolation. Every candidate is positioned relative to everyone else who interviewed for the same role. As more people interview, Λ-CORE sharpens the rankings. What your team sees is an ordered argument, not a list.
aperture/Applications
AA
TOTAL
34
All applications
NEW
1
Awaiting review
INTERVIEWS
14
Scheduled
AVG SCORE A-CORE
50%
Across 33 scored
candidatejobmatchappliedstage
aperture/Applications / Alex Meridian
AA
AM
Alex meridian
Senior software engineer · seattle, us
Resume Analysis
Recommendation: 18282%
Overall score
Skills30/35
Experience25/30
Education12/20
Strengths
strong academic background in cs
experience with compliance frameworks
proficiency in automation tools
Concerns
less than 2 years direct experience
limited exposure to privacy regulations
no cross-functional leadership evidence
interview.aperturehq.org/abc-defg-hij
Λ-CORE · LIVE
COG
6.4
DOM
5.8
COM
6.7
BEH
6.1
COL
5.3
ADP
6.5
signals847
APERTURE AI
Hi Alex, great to connect! Can you tell me about a time you had to make a critical technical decision with incomplete information?
Aperture AI
Alex S.
9:41 AM
Leave
E2EAES-256
aperture/Applications / Alex Meridian
AA
Composite Score
A-CORE91/100
SCORES
ATS Score82%
A-CORE Interview96%
A-CORE Composite91%
Λ-CORE dimensions
COG9.6
DOM9.2
COM9.8
BEH9.5
COL9
ADP9.7
aperture/Applications
AA
TOTAL
312
SHORTLISTED
3
REVIEW
2
AVG A-CORE
90%
candidatejobscorestage
CK
Christina kay
sr. engineer
96%
shortlistedAM
Alex meridian
sr. engineer
91%
shortlistedHF
Harold frank
sr. engineer
84%
shortlistedJE
Jose eusebio
sr. engineer
73%
reviewMM
Madelyn morris
sr. engineer
65%
reviewscroll
Want the full picture?
Inside λ-CORE: how the scoring model works.
The full model. live evaluation output, the six behavioral dimensions, how confidence intervals work, real-time pool rankings, and what that means for the decisions your team actually makes.
λ-CORE·Σ-10
What λ-CORE measures, and what it ignores.
aperture evaluation engine
6 Signals · 10-point BARS scale
Candidate #247 · sr. Software engineer · pool: 312
Live
Evaluation output
94Th percentile
COG6.4[5.9-6.8]High reliability
DOM5.8[5.2-6.3]Moderate
COM6.7[6.3-7.0]High reliability
BEH6.1[5.6-6.5]Moderate
COL5.3[4.8-5.7]Low reliability
ADP6.5[6.0-6.9]High reliability
P(top 5%)
0.0%
Σ-10 composite
6.21
Pool rank
#2 / 312
▌Evaluation complete_
&Ldquo;Every score comes with a confidence range. Because a single number without context is just a guess.”
The model
The six dimensions λ-CORE scores every candidate on.
6 signals
COG
reasoning under pressure, problem decomposition, logical coherence
DOM
role-specific knowledge depth and applied technical fluency
COM
clarity, structure, and precision in conveying complex ideas
BEH
work ethic, ownership signals, and response to adversity
COL
cross-functional effectiveness and collaborative instincts
ADP
comfort with ambiguity, learning velocity, and context-switching
How scoring works
A score with a confidence range, not just a number.
Every score comes with a range that shows how confident we are. As more candidates finish the same role, that range narrows, the more data we have, the more precise the score.
Score: 6.21 ± 0.36
Range: [5.82, 6.54]
Confidence: High
Decision zones
Rank 9 vs. Rank 11 is often a coin flip.
When two candidates are too close to call, we flag them as tied, because picking one over the other without enough data isn't a decision, it's a guess.
Clear hire
Borderline
No signal
Candidate rankings
Every new candidate re-sorts the shortlist.
Sr. Software engineer · 312 candidates · aperture λ-CORE
Updating
#CandidateScoreTop 5%
1
Priya sharma
hireSr. Engineer
COG7.1COM7.4DOM6.8·Sr. Engineer
moderate[6.4-7.0]
Confidence94%
Change+0.11
6.71/10
96.1%
Percentile
2
Alex meridian
hireStaff engineer
COG7.0COM6.9DOM6.5·Staff engineer
narrow[6.3-6.8]
Confidence91%
Change+0.04
6.54/10
91.4%
Percentile
3
Jordan wu
hireSr. Engineer
COG6.5COM6.3DOM6.2·Sr. Engineer
narrow[6.0-6.4]
Confidence88%
Change+0.11
6.21/10
87.2%
Percentile
4
Tariq okonkwo
reviewPlatform engineer
COG6.1COM5.8DOM6.3·Platform engineer
moderate[5.7-6.3]
Confidence79%
Change+0.08
5.98/10
79.5%
Percentile
5
Maya chen
reviewBackend engineer
COG5.9COM5.7DOM6.0·Backend engineer
moderate[5.5-6.1]
Confidence72%
Change+0.03
5.83/10
72.1%
Percentile
6
Sven eriksson
reviewSr. Engineer
COG5.7COM5.5DOM5.8·Sr. Engineer
moderate[5.3-5.9]
Confidence65%
Change+0.11
5.61/10
63.4%
Percentile
7
Nia patel
watchInfrastructure eng.
COG5.4COM5.3DOM5.6·Infrastructure eng.
wide[5.1-5.8]
Confidence54%
Change+0.14
5.44/10
54.7%
Percentile
8
Omar hassan
watchFullstack engineer
COG5.0COM5.2DOM5.1·Fullstack engineer
wide[4.7-5.5]
Confidence43%
Change+0.22
5.12/10
41.2%
Percentile
Summary
3 Clear hire
3 review
2 watch
Avg score 5.93Showing 8 / 312
Built-in pipeline
Move candidates through stages here, or in the ATS you already use.
applied
3SK
Sarah kim
Sr. Engineer
RP
Raj patel
Backend eng.
EL
Emma liu
Sr. Engineer
+ more
screening
3PS
Priya sharma
Sr. Engineer
6.7Hire
AM
Alex meridian
Staff eng.
6.5Hire
JW
Jordan wu
Sr. Engineer
6.2Hire
+ more
interview
2TO
Tariq okonkwo
Platform eng.
6.0Review
MC
Maya chen
Backend eng.
5.8Review
offer
1LV
Lena vogt
Sr. Engineer
7.1Hire
12 Scored by aperture
4 Recommended
Live
See your numbers
How much time resume screening costs your team.
Your hiring pipeline
Open roles right now3 roles
Applicants per role80 applicants
Estimates based on industry averages for manual screening workflows. Your actual numbers may vary.
Recruiter hours saved
202Hrs
Per hiring cycle
Days to shortlist
14
2Days
Interviews avoided
51
Unnecessary screens
Candidates shortlisted
Top 5% · ready for your team to review
12
hire who is right.
Switch it on today. Tomorrow you read a shortlist, not a pile.
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