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After briefly getting acquainted, we’ll show you how Faros AI helps:

  • Boost velocity, quality and efficiency in developer workflows
  • Maximize AI’s impact on productivity
  • Improve delivery and resource allocation
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Measure the impact of AI coding assistants

Understand the impact on developer productivity and satisfaction. Leverage holistic ROI dashboards to communicate the value, monitor the impact, and optimize your rollout. 

Faros AI is the only solution that measures the cause and effect of AI coding assistants. Get the confidence you need. Accelerate AI adoption. Outpace your competition.

Charts tracking GitHub Copilot's usage and impact on velocity and quality

Analytics that go far beyond the basics
available from the coding assistants

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Faros AI
AI Coding Assistant

Adoption metrics

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Granular

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Coarse

Usage tracking

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Full data history

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Partial

Downstream impact metrics

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Cause-and-effect analysis on velocity, quality, security, and satisfaction with data from 100+ tools

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Limited

Team and Power User views

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A/B Testing and Before/After analysis

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Out-of-the-box dashboards for tracking adoption, impact, risk and value

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Out-of-the-box developer surveys

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Team-tailored alerts and recommendations to address new bottlenecks

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The right implementation can unlock 40% higher ROI

New AI coding assistants and editors are being announced daily. Improve your return on investment with a full measurement framework that guides you from pilot to rollout to optimization.

Vendor Comparison

Vendor comparison

Which is the best AI coding assistant?

Identify the most effective tool,
best-suited to your code and favored by your developers.

A/B Testing

A/B testing

is the tool worth it?

Observe the impact of AI augmentation on different cohorts and profiles.

Before and After

Before and after

What’s changed?

Measure the time savings. Calculate the economic benefit. Spot shifting bottlenecks.

Discover the Engineering Productivity Handbook

How to build a high-impact program that drives real results.

What to measure and why it matters.

And the 5 critical practices that turn data into impact.

Shai Peretz - Testimonial Image
While these tools have the potential to increase productivity, having a way to evaluate their impact scientifically will help build the business case for the investment.
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Shai Peretz
SVP Engineering @ Riskified

Track adoption and usage over time

Without usage, there can be no ROI. Optimize your rollout for higher impact with insights into adoption and usage.

Measure daily, weekly, and monthly adoption.

Track acceptance rates and lines of code generated, by language
and editor.

Measure the percentage of AI-generated code by repo.

See which activities are augmented most: coding, testing,
debugging, documenting, etc.

Identify unused licenses and power users who can train others.

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AI Copilot Image

Measure downstream impacts across the entire SDLC

Separate hype from reality and set the right expectations for your org.

Identify emerging bottlenecks in code review, deployment, or QA.

Maintain visibility into quality, reliability, and tech debt.

Identify potential security and compliance issues in AI-augmented code.

Present clear cost/benefit metrics to executive leadership and finance.

Understand time savings and developer satisfaction

See how accelerated coding is changing development patterns.

  • Measure cumulative time savings and understand where they’re being reinvested (velocity, quality, tech debt, upskilling, etc.).
  • Capture developer sentiment with out-of-the-box surveys.
  • Understand which teams and roles are adopting and benefiting most.
  • Improve training and mentoring to optimize your rollout.
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Frequently asked questions

What type of impact metrics are included?
How does Faros AI help in head-to-head AI code assistant evaluations?
How does Faros AI help with driving adoption?
How can Faros AI help if the pilot has already begun?
Is the data in the Faros AI ROI dashboards anonymized?
Is Faros AI secure?
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Ready to measure the impact of AI coding assistants?