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AI Framework

Scale AI Coding Safely

The CodeHealth™ AI Performance Framework enables teams to safely adopt and scale AI coding, without amplifying technical debt.
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AI amplifies defects in unhealthy code

Our peer-reviewed, large-scale research shows defect risk increases by at least 60% when AI works on unhealthy code.

Velocity gains disappear within months

Research shows initial AI velocity gains are cancelled out within months due to rising code complexity.

AI performs worst in legacy code

AI accelerates output but lacks structural context, amplifying defect risk in unhealthy systems.

Technical debt is compounding

75% of tech leaders will face critical technical debt by 2026. AI acceleration without quality control compounds that risk.

Empowering the world's top engineering teams
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The CodeHealth™ AI Performance Framework

Get AI speed, without the risk

We built a metric that predicts AI performance, then productized it so teams can scale AI coding without compromising quality.
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Assess AI risk

The CodeHealth™ analysis identifies the code ready for AI acceleration versus parts of the code that are not-yet AI-ready.

Safeguard AI-generated code

Integrate the CodeHealth™ MCP to safeguard any AI-generated code to ensure it stays AI-friendly. Automatically fix new issues.

Make unhealthy code AI-ready

Use the MCP server to first uplift unhealthy code and make it AI-ready, and safely expand AI utilization across the codebase.

20%

Without structural guidance, frontier models fix only ~20% of the code health issues.

See the results

90-100%

With MCP-augmented CodeHealth™ guidance, fix rates reach 90–100%.

See the benchmarks

60%

60% lower defect risk when AI works on healthy code.

Read our research

Where will AI Increase Defect Risk?

Get the AI Playbook to understand where AI will fail and how to safely apply and scale AI coding in your team.

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Packages

The CodeHealth AI Performance Framework

Assess AI risk, uplift unhealthy code and safely scale AI-assisted development  across your codebase. 
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CodeHealth™ MCP Server

For Devs, Single User License

Safeguard AI-Generated Code, uplift unhealthy code
This package includes:
Real-time CodeHealth™ checks of AI-generated code
Detects maintainability risks
Structured feedback instructing AI how to fix issues
Automated, self-correcting quality loop
Works with any AI coding assistant
Guides AI to refactor more reliably with objective feedback
Model-agnostic and agent-ready
Read more

Featured

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CodeHealth™ MCP Server + Full CodeScene product

Package Teams 10+

Scale AI safely at scale, measure Impact, reduce risk.

This package includes:

CodeHealth™ analysis across the entire codebase
AI-readiness assessment
Identification of high-risk AI areas and hotspots
Strategic AI governance and safe adoption planning
Automated Code Health reviews in PRs/MRs
Local quality gates in IDEs
Code coverage gates in pull requests
Measure AI adoption and delivery throughput
Track ROI and impact over time
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Add-on / PR-integration / Code Coverage

Additional power features and safeguards

Beyond the core AI safeguards, CodeScene includes additional features that strengthen quality, reduce risk, and give teams and leaders full visibility.
codescene bot commented on Apr 18, 2020

Enforce code coverage on new and changed code

CodeScene comes with code coverage gates. Use them as an additional safety-net for your engineering team in Pull and Merge Requests.

Automated CodeHealth™ reviews in Pull Requests

CodeScene's Pull and Merge Request integration adds an extra safeguard to ensure no new technical debt slips through the cracks.

CodeHealth™ Feedback in the IDE

Shift left on code quality with real-time CodeHealth™ guidance in the editor, enforcing quality gates locally.

Software Portfolio Overview

CodeScene's Software Portfolio Overview lets you assess AI-readiness at the enterprise scale. CodeHealth™ KPIs for all your products and software components.
See our software portfolio
Demo Projects
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Make a business case for code quality

Translate Code Health scores into business value

How much can you gain by improving your code health? Use the calculator to estimate your ROI in terms of faster development and fewer defects.
↑ Productivity Gain/Loss
(Dev Impact (%): Speed & Defect Count)
Code Health →
(Low scores lead to longer dev time)
Set your Code Health score: 5.4
Projected Code Health score over time: 7
Estimate average unplanned work (%)
Productivity Gain (ROI): 0%
Our award-winning model translates Code Health into business value and received Best Paper at the 7th ACM/IEEE TechDebt Conference. Read the peer-reviewed study: "Increasing, not Diminishing: Investigating the Returns of Highly Maintainable Code"

AI coding assistants & IDEs

Designed for agentic workflows and composable AI tooling, not tied to any single editor, assistant or model.
GitHub Copilot GitHub Copilot
Cursor Cursor
ChatGPT ChatGPT
Codeium Codeium
Windsurf Windsurf
Claude Code Claude Code
Also works with other AI coding assistants via MCP, including Amazon Q, Gemini Code Assist, Tabnine, Sourcegraph Cody and open-source tools.
JetBrains JetBrains
Visual Studio Code Visual Studio Code
Visual Studio Visual Studio

Ready to Scale AI coding?

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Solving AI's 3 hardest problems

Safeguard AI output, uplift legacy code and measure the real impact of refactoring.

Safeguarding AI

The CodeHealth™-aware MCP server continuously evaluates AI-generated changes against objective maintainability signals and feeds structured feedback back to the AI when risk increases. This creates a deterministic self-correcting loop that delivers easy to evolve code.

Uplift Unhealthy Code

AI refactoring quality improves when code is modular and easy to reason about. The MCP Server guides AI assistants through Code Health reviews, identifies targeted design issues, and enables refactoring in small, measurable steps, verified by CodeHealth™. For large legacy functions, CodeScene ACE can accelerate the initial restructuring into smaller, cohesive units.

ROI of Refactoring

CodeScene links CodeHealth™ scores to business outcomes via a validated statistical model. The MCP server exposes ROI impact on velocity, defect rates, and maintenance costs, so you can estimate how improving Code Health affects delivery and justify refactoring.

Frequently asked questions

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CodeHealth provides an objective signal about the maintainability and change risk of code. Through the MCP Server, agents can run tools like code_health_review to assess the quality of a file and identify concrete maintainability issues.


The Code Health score gives agents a measurable goal, while the review highlights specific problems to fix. This allows agents to plan structured refactorings instead of guessing at improvements.


Read Agentic AI Coding: Best Practice Patterns for Speed with Quality.

Unhealthy code is not AI-ready. Low Code Health increases the likelihood that agents fail on their task or, increase defects and at best, burn excess tokens.

We know from our peer-reviewed research that AI performs better in healthy code, in fact, the defect risk increases with at least 60% when the code is unhealthy. Your goal is to aim for AI-ready code that has a code health of 9.5, ideally 10. Code that is not-yet AI-friendly needs to be refactored and uplifted before attempting to implement features via agents.

The AI-generated code is evaluated by the Code_health_review, via the MCP server, which assesses the quality of a file and identifies concrete maintainability issues.

Read the research
CodeScene uses CodeHealth™ as a proxy for AI readiness. CodeHealth aggregates multiple structural maintainability factors to measure how easy code is to understand, modify, and evolve. CodeHealth is an aggregated metric based on 25+ factors scanned from the source code. The code health factors correlate with increased maintenance costs and an increased risk for defects, evaluated and proven in our peer-reviewed research “The Business Impact of Code Quality”. Read more.

Code with high CodeHealth scores is typically modular and easier to change, which makes it safer and more reliable for AI agents to work with.

Our Code Health Score scale goes from 1 to 10. Healthy code with low risk is from 9+ on the scale, but for AI-ready code, it’s 9.5, ideally a perfect 10.

Problematic code, with increased maintenance efforts, increased technical debt is between 4-8.9, Unhealthy code is ranked between 1-3.9 and means severe technical debt, unhealthy, expensive code to maintain.
You want to aim for a Code Health of at least 9.5, ideally a perfect 10.0 for AI-ready code.

In our research “Code for Machines, Not Just Humans: Quantifying AI-Friendliness with Code Health Metrics” we show that when AI-coding assistants and agents operate on unhealthy code, the defect risk increases by at least 60%. As the code health decreases, we also see that defect rates rise sharply in deeply unhealthy, tangled code. Agents get confused by the same patterns as humans.

Read the research

Many legacy functions are too large and complex for reliable AI-assisted work. That leads to higher error rates, wasted tokens, and fragile changes. Without objective feedback, AI agents often end up rearranging complexity or making superficial improvements instead of meaningfully improving maintainability.

The MCP server changes that by giving the agent deterministic guidance through a code_health_review. The Code Health score provides a clear, measurable target, while the review explains the specific maintainability issues that need attention. This lets the agent build a structured refactoring plan based on evidence rather than guesswork.

The workflow is simple: review → plan → refactor → re-measure.

For very large legacy functions, the first step is often to break them into smaller, more cohesive units. That increases modularity and makes further AI-assisted refactoring far more reliable. The result is higher Code Health, clearer code intent, and a larger AI-ready surface where agents can work safely and effectively.

The MCP Server prevents AI tools from introducing technical debt by surfacing maintainability issues such as high complexity, deep nesting, and low cohesion.

It enforces deterministic, objective code health metrics through the MCP Server, triggering a self-correcting refactoring loop when quality issues are detected. This ensures that every AI-generated change is evaluated and does not introduce additional risk. If a defect risk is detected, the MCP Server prompts the AI agent to adjust the code and then reassess it through a Code Health review, which is part of the mandatory workflow.

As a first line of defense, this automated feedback loop safeguards production code by preventing AI agents from introducing technical debt.