A linear, calibrated pipeline — set the rules once, share a link, and let Nasiya score, surface, and explain every applicant who walks through the door.
1
Step 1 · Org Setup
Industry Context
01 → 02Context injected into every analysis
2
Step 2 · Role Creation
Job Description
AI Parse
Manual
Nasiya will extract requirements automatically.
Extracted Requirements
SQL fluency
Pakistan FMCG market experience
Master's-level qualification
Add requirement
Omni-channel campaign experience
VP-level reporting line
Add
02 → 03Role created, calibrate scoring
3
Step 3 · Calibration
Scoring Thresholds
AI Threshold65%
Advance if score ≥ 65
ATS Threshold50%
Keyword floor
!Candidates within 5 points of AI threshold are flagged as Borderline.
14 claims · ↑ High trajectory · Applied 2 days ago
Strong Alignment
Sara Malik
11 claims · ↗ Rising · Applied 3 days ago
Recommended
Zeeshan Majeed
9 claims · → Steady · Score 4 pts below threshold
Borderline ⚠
Bilal Raza
6 claims · → Flat · Applied 5 days ago
Weak
07 → 08Click any candidate to open their card
8
Step 8 · Candidate Card
Candidate Card
Ahmad Khokhar
SHORTLISTED · ADVANCE
Nasiya Score82%
Candidate Brief
3 years leading product at a B2B SaaS, shipped 4 major features, managed cross-functional team of 8.
Claims
Questions
"Grew ARR from $1.2M to $4.1M in 18 months"
Low Risk
Quantifiable ImpactVerifiability 8/10
↳ You said ARR grew 3x — what was the base figure and what specifically changed in the sales motion?
Proof: Portfolio, metrics reportWatch for: Inflated attribution, no base number
"Led team of 12 across 3 time zones"
Medium Risk
Leadership & ManagementVerifiability 6/10
↳ Walk me through a disagreement between Lagos and Karachi — how did you resolve it without escalating?
Proof: Org chart, referencesWatch for: Reported headcount vs. true ownership
"Built ML pipeline from scratch"
High Risk
Technical ProficiencyVerifiability 3/10
↳ What tech stack did you use, what scale did it run at, and did you build it solo or lead a team?
Proof: Github, architecture diagramWatch for: Solo claim vs. contributed-to
Q1
"You said ARR grew from $1.2M to $4.1M — what was the starting sales cycle length and what specifically changed in the motion?"
Re: Grew ARR 3x claim
Why it matters: Tests whether the candidate owned the lever or just observed it.
Q2
"Walk me through the moment the ML pipeline underperformed. What was your first call, and who did you bring in?"
Re: Built ML pipeline from scratch
Why it matters: Distinguishes builders from observers.
Q3
"Your team spanned 3 time zones — how did you handle a disagreement between Lagos and Karachi without escalating?"
Re: Led team of 12 across 3 time zones
Why it matters: Tests real leadership vs. reported leadership.
08 → 09Ask Nasiya anything about this candidate
9
Step 9 · Ask Nasiya
Ask Nasiya
N
Nasiya
BETA
Ahmad Khokhar
82%14 claims↑ High trajectory
Which claims need the most scrutiny?
Two claims stand out. The ARR growth claim (8/10 verifiability) has no base figure — probe the starting number and what specifically changed. The ML pipeline claim is high risk (3/10 verifiability); ask for tech stack, scale metrics, and whether they built it solo or led a team. The leadership claim is medium risk — the CV shows overlapping dates with an individual contributor role at the same company.
What should I prioritise in Round 2?
Pressure-test the ARR claim first — it's the anchor of his entire impact narrative. If that holds, the leadership claim becomes secondary. Skip the education section entirely; it's fully verifiable and already at 9/10. Focus 70% of Round 2 on the two commercial claims.