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Enterprise AI Proficiency Measurement

Your team adopted AI tools.
Now measure if it's working.

Pairbility is the measurement layer for enterprise AI proficiency. We instrument how engineers collaborate with AI — across 8 dimensions and 40+ sub-skills — so you can answer the question your board keeps asking.

8
Proficiency Dimensions
40+
Measured Sub-skills
10 min
Agent Deployment
Zero
Workflow Disruption

Enterprises are spending millions on AI tools with no way to measure ROI.

60%
of engineering leaders cite lack of clear metrics as their #1 AI challenge
LeadDev 2025 AI Impact Report, 880 leaders surveyed
19%
slower — experienced developers using AI took longer than those without it in a controlled trial
METR Randomized Controlled Trial, 2025
73%
of engineering leaders say AI has changed the skills they look for when hiring
LeadDev 2025 AI Impact Report
0
established industry standards exist for measuring human-AI collaborative proficiency
ISO / IEEE / WEF review, 2026
How It Works

Deploy in minutes. Measure what matters.

A lightweight MCP agent integrates with your team's existing AI tools. An on-premise Edge Server scores everything locally — no sensitive data leaves your network.

01

Deploy the Agent

Install the Pairbility MCP agent into Cursor, Copilot, or Claude Code. 10-minute setup, zero disruption to engineer workflows. Works with the tools your team already uses.

02

Capture Interaction Patterns

The agent observes how engineers collaborate with AI — prompt quality, critical review of suggestions, model switching, error detection. Behavioral signals, not just activity metrics.

03

Score & Benchmark

The Edge Server scores each engineer across 8 dimensions and 40+ sub-skills. Team heatmaps, adoption trends, proficiency distributions — the data your CTO needs for the board.

04

Drill Down & Act

Interactive dashboards let managers drill into team and individual profiles. Identify who needs coaching, where AI adoption stalls, and which teams are getting real ROI — then track improvement over time.

The Framework

8 dimensions of AI collaboration proficiency.

Not activity metrics. Not code output. The behavioral patterns that separate engineers who are genuinely more productive from those who aren't.

D1
Prompt Engineering
Quality, specificity, and iterative refinement of AI instructions
D2
AI Output Integration
How effectively AI suggestions are adapted and incorporated
D3
Problem Decomposition
Breaking complex work into AI-appropriate subtasks
D4
Context Management
Providing and maintaining effective context for AI tools
D5
Critical Review
Evaluating AI output for errors, hallucinations, and quality
D6
AI Utilization Efficiency
When and how often AI is leveraged for appropriate tasks
D7
Error Diagnosis
Identifying and correcting AI mistakes with precision
D8
Workflow Orchestration
Orchestrating multi-step workflows combining human and AI effort
The Founder

Built by someone who lived the problem.

Yang Jing
Founder & CEO
20+ years software engineering
7 years at Salesforce
3 years at LendingClub
Patents filed with USPTO

I watched engineering orgs struggle to measure developer effectiveness for two decades. When AI coding tools arrived, the problem exploded — teams adopted Copilot and Cursor with zero way to know if it was helping or hurting.

I realized the entire assessment industry was built for a world that no longer exists. Every coding test assumes a human working alone. That's not how anyone works anymore. Pairbility is the measurement layer for the AI era.

The board is asking about AI ROI.
Give them the answer.

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