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No more blind spots in AI adoption
Get the live dashboard that shows how AI coding assistants impact your team across projects, tasks, and developers.
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Benefits
Measure impact, then multiply it
Understand GenAI usage
Track adoption and scale what works
See AI at work
Measure usage, code share, speed
Zoom into AI work
Measure AI usage and delivery outcomes
Why it matters
You can’t fix what you can’t see
Unknown outcomes
Most teams don’t know if GenAI speeds things up or slows them down.
Unknown outcomes
Most teams don’t know if GenAI speeds things up or slows them down.
Scaling blind
Managers can’t prove ROI, spot bad patterns, or scale good ones.
Scaling blind
Managers can’t prove ROI, spot bad patterns, or scale good ones.
Invisible costs
Without visibility, you risk wasted budget and hidden delivery risk.
Invisible costs
Without visibility, you risk wasted budget and hidden delivery risk.
Features
All your GenAI answers in one place
Usage trends
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Performance signals
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Task analysis
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Task-level activity
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Usage trends
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Performance signals
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Task analysis
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Task-level activity
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How it works
From plug-in to clarity – fast
1. Connect
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2. Track
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3. Explore
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1. Connect
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2. Track
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3. Explore
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Demo
See it in action
Don't just measure impact, amplify it
Don't just measure impact, amplify it
Explore the full platform
Explore the full platform
What people are saying
Everyone is trying to measure GenAI's impact
“A McKinsey study found that gen AI can significantly improve developer speed for some tasks – for example, code documentation (45–50% faster), code generation (35–45% faster), and code refactoring (20–30% faster)”
McKinsey research, 2024
“By incorporating GitHub Copilot into Visual Studio Code… we saw programmers reduce ten-minute tasks, such as writing a small function, down to the 30 seconds it took to write a comment that explains the function. Often these Copilot functions work out of the box without any need for changes.”
Joe Walsh, Principal Technologist at Launch Consulting
“A McKinsey study found that gen AI can significantly improve developer speed for some tasks – for example, code documentation (45–50% faster), code generation (35–45% faster), and code refactoring (20–30% faster)”
Inbal Shani, Twilio Chief Product Officer (2025)
“Companies are no longer as limited by the number of engineers they can hire, but rather by the degree to which they can augment them with AI…”
DX “AI Measurement Framework” report, 2025
“Code copilots lead the charge with 51% adoption, making developers AI’s earliest power users. GitHub Copilot’s rapid ascent to a $300 million revenue run rate validates this trajectory.”
Menlo Ventures “State of GenAI in the Enterprise report
“More than 97% of respondents… said they had used AI coding tools at work, but only 38% of US-based developers said their organizations actively encouraged adoption.”
GitHub’s AI in Software Development 2024 survey
“A McKinsey study found that gen AI can significantly improve developer speed for some tasks – for example, code documentation (45–50% faster), code generation (35–45% faster), and code refactoring (20–30% faster)”
McKinsey research, 2024
“By incorporating GitHub Copilot into Visual Studio Code… we saw programmers reduce ten-minute tasks, such as writing a small function, down to the 30 seconds it took to write a comment that explains the function. Often these Copilot functions work out of the box without any need for changes.”
Joe Walsh, Principal Technologist at Launch Consulting
“A McKinsey study found that gen AI can significantly improve developer speed for some tasks – for example, code documentation (45–50% faster), code generation (35–45% faster), and code refactoring (20–30% faster)”
Inbal Shani, Twilio Chief Product Officer (2025)
“Companies are no longer as limited by the number of engineers they can hire, but rather by the degree to which they can augment them with AI…”
DX “AI Measurement Framework” report, 2025
“Code copilots lead the charge with 51% adoption, making developers AI’s earliest power users. GitHub Copilot’s rapid ascent to a $300 million revenue run rate validates this trajectory.”
Menlo Ventures “State of GenAI in the Enterprise report
“More than 97% of respondents… said they had used AI coding tools at work, but only 38% of US-based developers said their organizations actively encouraged adoption.”
GitHub’s AI in Software Development 2024 survey
Waitlist
Sign up for early access
Join the waitlist to get access
200+ people on the waitlist
FAQs
What tech leaders want to know
Engineering Manager
It tracks how GenAI affects real engineering work — coding, testing, and reviews — showing concrete gains in speed, quality, and reduced rework.
How does this tool measure productivity?
Engineering Manager
It tracks how GenAI affects real engineering work — coding, testing, and reviews — showing concrete gains in speed, quality, and reduced rework.
How does this tool measure productivity?
Chief Technology Officer
Absolutely. It compares adoption and impact across tools so you know which deliver value and which don’t justify their cost.
Will it help us decide which GenAI tools to keep?
Chief Technology Officer
Absolutely. It compares adoption and impact across tools so you know which deliver value and which don’t justify their cost.
Will it help us decide which GenAI tools to keep?
Engineering Manager
Baseline data appears in days, early trends in weeks, and within a quarter you’ll see a clear ROI and adoption story.
How quickly will we see insights?
Engineering Manager
Baseline data appears in days, early trends in weeks, and within a quarter you’ll see a clear ROI and adoption story.
How quickly will we see insights?
Engineering Lead
Yes. It translates engineering improvements into business outcomes like faster delivery, lower costs, and fewer defects in production.
Can it show ROI?
Engineering Lead
Yes. It translates engineering improvements into business outcomes like faster delivery, lower costs, and fewer defects in production.
Can it show ROI?
Executive
The tool monitors for issues like code churn, poor quality output, or unusual usage patterns, helping teams set safe AI governance policies.
How does it handle risks?
Executive
The tool monitors for issues like code churn, poor quality output, or unusual usage patterns, helping teams set safe AI governance policies.
How does it handle risks?
Engineering Lead
Yes. It combines usage data with lightweight surveys, showing both adoption levels and how developers feel about GenAI tools.
Can it measure developer adoption and experience?
Engineering Lead
Yes. It combines usage data with lightweight surveys, showing both adoption levels and how developers feel about GenAI tools.
Can it measure developer adoption and experience?
Chief Investment Officer
Other tools track outcomes like DORA metrics. This isolates GenAI’s unique impact on speed, quality, and developer experience.
How is this different from DevOps dashboards?
Chief Investment Officer
Other tools track outcomes like DORA metrics. This isolates GenAI’s unique impact on speed, quality, and developer experience.
How is this different from DevOps dashboards?
AmplifyGenAI
AmplifyGenAI
AmplifyGenAI
AmplifyGenAI
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Powered by CodeTogether – by developers, for developers.
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