See how your org's real AI fluency maps out
Workforce Intelligence points the same instrument you use to hire at your existing engineers — with consent. Scroll down to explore the team map, the signals behind it, and the employee self-service view.
How it works
From consent to team-level insight
Passive monitoring during real development work — not surveillance. Engineers opt in, leaders get aggregate maps, and everyone can see what was captured.
Engineers opt in
Workforce capture is off by default. Each engineer grants explicit consent before any passive monitoring begins — and can revoke at any time.
Real work, real signals
The VS Code plugin captures how work happens during normal development — delegation cadence, verification discipline, tooling patterns — never keystroke content.
Leaders see the map
Org admins get a k-anonymized team map: verdict distribution, dimension heatmaps, gap flags, and trends — the same scoring engine used in hiring assessments.
Signals you can understand
What the map actually tells you
Every number traces back to behavioral evidence from consented work sessions — the same dimensions scored in Copilot Command assessments.
Verdict distribution
How engineers on a team actually work with AI — from force multipliers who command it well to rubber stampers who accept output without verification.
Example: Platform Engineering: 6 competent orchestrators, 1 rubber stamper
Gap flags
Automated patterns that surface coaching opportunities — like high delegation paired with low verification discipline.
Example: High delegation, low verification discipline
Dimension heatmap
Scores across velocity, execution discipline, collaboration, tool mastery, verification discipline, and delegation quality.
Example: Product Engineering tool mastery: 81 · verification discipline: 68
Trends over time
Period-over-period average scores so you can measure whether targeted development or tooling investments are moving the needle.
Example: Platform Engineering: 72.8 → 76.4 over two quarters
Verdict labels in this demo
For your organization
How workforce intelligence helps engineering leaders
Development and tooling decisions — not performance management. The map is designed to coach teams, justify AI investments, and align hiring with how your org actually works.
Answer the AI-tooling ROI question
See which teams turn Copilot and other AI tools into leverage — and which teams rubber-stamp output. Finally, a data-backed answer to "is our AI spend paying off?"
Target development where it matters
Gap flags like "high velocity, low verification discipline" tell engineering leaders exactly where to coach — not who to rank or fire.
One instrument for hire and grow
The same behavioral SIEM that scores candidates in assessments produces workforce profiles. Your hiring bar and your team map speak the same language.
Employees stay in control
Engineers see their own profile, export their data as JSON, and revoke consent instantly. Trust is built in — not bolted on after the fact.
Demo mode
This page uses canned org data. The panels below are the same views org admins and employees see in production — nothing is simulated on the frontend beyond the sample dataset.
What you are looking at
Acme Corp's engineering org has three teams with enough consented engineers to meet the k=5 anonymity threshold. Platform Engineering shows a delegation–verification gap; Product Engineering is fast but light on verification discipline; one smaller team is suppressed from this view. Panel 2 shows what an individual engineer sees about their own profile.
Panel 1
The org admin view
Engineering leaders see team-level AI-fluency maps — verdict mix, gap flags, dimension scores, and quarter-over-quarter trends. Teams below the k-anonymity floor are hidden automatically.
Team AI-fluency map
Aggregate-only view · teams below k=5 are hidden (1 suppressed)
Platform Engineering
Verdict distribution
Gap flags
- High delegation, low verification discipline
Dimension heatmap
Product Engineering
Verdict distribution
Gap flags
- High velocity, low verification discipline
- Low AI tool and learning signals
Dimension heatmap
Data & ML
Verdict distribution
Gap flags
- Low AI tool and learning signals
Dimension heatmap
Trends
| Period | Team | Engineers | Avg score |
|---|---|---|---|
| 1/1/2026 – 3/31/2026 | Platform Engineering | 14 | 76.4 |
| 1/1/2026 – 3/31/2026 | Product Engineering | 22 | 79.1 |
| 10/1/2025 – 12/31/2025 | Platform Engineering | 13 | 72.8 |
| 10/1/2025 – 12/31/2025 | Product Engineering | 20 | 74.3 |
For team development only — not for performance management, ranking, or termination decisions.
Panel 2
The employee self-service view
Every engineer can view their own AI-fluency profile, export it as JSON, and grant or revoke monitoring consent. In this demo, consent controls are read-only.
Your workforce profile
View and export your own AI-fluency signals from consented real-work sessions. You can revoke consent at any time.
Monitoring consent
Active consent granted on 3/15/2026 (doc v1.0).
Sessions
12
Average score
81.3
Latest verdict
Competent orchestrator
Your dimension scores
Ready to map AI fluency across your engineering org?
Workforce Intelligence is consent-gated and off by default. Enable it when your team is ready — we will help you roll it out responsibly.