What is the Simulator?
The AI Talent Pool Simulator is a scientific validation tool built into HCM-AO that lets HR teams and AI engineers test the system's reliability before using it with real candidates. It generates realistic synthetic CVs, runs them through the same AI parsing pipeline that processes real CVs, and measures how accurately the AI performs.
The Interface At A Glance
┌─────────────────────────────────────────────────┐ │ 🧪 AI Talent Pool Simulator & Stress-Tester │ ├─────────────────────────────────────────────────┤ │ [Bell Curve Visualisation] │ │ │ │ μ ──────────────────── 10 ← mean YOE target │ │ σ ──────────────────── 3 ← diversity level │ │ ⚠️ [Stress Level Indicator] │ │ │ │ [Core Skills Count: 5 ▾] [Count: 20] │ │ │ │ 📄 Target Job Description (Required) │ │ [ Drop or click to upload ] │ │ │ │ [Generate Mock CV Batch] │ │ │ │ [📦 Export ZIP] [🚀 Run in S&R Mode] │ └─────────────────────────────────────────────────┘
Step 1 — Upload Your Target Job Description
Before generating CVs, upload the job offer file (PDF or TXT). The Simulator uses this to extract required skills, calibrate skill matching per candidate, and provide job context to the Certificate.
Step 2 — Set the Mean (μ) — "What kind of candidates?"
Move the μ slider to set the average years of experience you expect in this talent pool.
| μ | Candidate Profile |
|---|---|
| 2–4 | Fresh graduates / entry-level |
| 5–8 | Mid-career professionals |
| 9–15 | Senior / specialist hires |
| 16–30 | Executive / director-level |
Example: For an Accounting Assistant role requiring 2–5 years, set μ = 3 or 4.
Step 3 — Set the Diversity (σ)
σ is the most powerful control. It has two simultaneous effects: talent pool diversity (spread of YOE values) and stress-test trap injection intensity.
σ Reference Table
| σ Setting | Mode | Trap Prob. / CV | Certificate? |
|---|---|---|---|
| 0 | Uniform Pool | 0% | ❌ |
| 1–3 | Low Entropy | 7%–20% | ❌ |
| 4–6 | Moderate Entropy | 27%–40% | ❌ |
| 7 | Near Threshold | ~47% | ❌ |
| ≥ 8 | 🔴 Stress Test Mode | 53%–80% | ✅ Yes |
| > 12 | ⚠️ Extreme / Pathological | >80% | ✅ Yes |
The Stress Level Indicator bar below the σ slider updates in real-time showing exactly the current trap probability and mode label.
The Three Trap Types
| Trap | What it is | Audit Metric |
|---|---|---|
| 🕵️ Hidden Keywords | Invisible white text keyword stuffing | SEO Resilience |
| ⚠️ Date Conflicts | Employment dates reversed (start > end) | Data Integrity |
| 🔡 Encoding Noise | Invisible Unicode zero-width characters in names | Parsing Accuracy |
Step 4 — Batch Size and Core Skills
- Count (default 20) — how many CVs to generate. Use ≥ 50 for a statistically meaningful stress test.
- Core Skills Count — how many JD-specific skills each candidate carries. Use 5 for most roles; 8–10 for highly technical roles.
Step 5 — Generate the Batch
Click "Generate Mock CV Batch". The system will:
-
Sample YOE values from the Gaussian distribution N(μ, σ²)
-
Build a full CV per candidate — name, experience, education, skills, projects
-
Inject adversarial traps probabilistically based on σ
-
Run the NLP parsing pipeline on every CV
-
Compare parsed results to known ground truths
-
Compute the three Real-Time Audit Metrics
Step 6 — Review Each CV
After generation, the Generated CVs Batch panel appears. Click any candidate row to open their full profile: name, email, phone, skills (showing JD skill matches), education, and experience.
Step 7 — Run in Selection & Recruiting Mode
Click "🚀 Run in Selection & Recruiting Mode" to feed the entire batch directly into the AI ranking engine — just as if they were real candidates applying for the job. This provides a full end-to-end impact view of what the algorithm would do with this talent pool distribution.
rank_candidates(), and populates the S&R Results
Dashboard — including Assessment Reports and AI-drafted Response Emails for each candidate.
Step 8 — Export as ZIP
Click "📦 Export All as ZIP" to download a .zip file with all generated
CVs as individual .txt files. This works entirely in your browser — no server
round-trip needed.
Step 9 — Audit Metrics Explained
Step 10 — Certificate of Algorithmic Robustness
Click "Issue Robustness Certificate" after a simulation to generate a formal certification document.
| Condition | Required Value |
|---|---|
| σ (stress level) | > 7 |
| Average of all 3 audit metrics | > 95% |
If either condition is not met, the certificate displays "⚠ NOT CERTIFIED" with the specific unmet criteria listed. Both conditions must be true simultaneously.
Frequently Asked Questions
Quick Reference Card
# WARM-UP — casual validation μ = role target YOE σ = 2–4 count = 10–20 → Confirms parser works on clean data # STANDARD TEST — pre-operational check μ = role target YOE σ = 5–7 count = 20–50 → Moderate traps. Gets meaningful Parsing Accuracy scores. # FULL STRESS TEST — certification eligible μ = role target YOE σ = 8–12 count = ≥ 50 → All trap types active. Certificate issued if avg accuracy > 95%.