Build faster with context that scales
When your engineers and AI tools have context, everything moves faster with
less risk. DevRamp embeds context into your workflow, giving humans and
machines the information they need to perform at their best.
Grow Context
Track who understands your code and how well.
DevRamp begins by analyzing your Git activity building a live context map. Every pull request, commit and review contributes to a real-time map of developer context.
Uncover key person risks
Highlight context gaps
Quantify code maintainability
Optimize Reviews
Turn code review into a context engine.
DevRamp recommends reviewers by actual familiarity, closing context gaps, surfacing learning opportunities, so every review augments your team’s context.
Select reviewers intelligently
Surface learning opportunities
Grow team-wide context
Expand Capacity
Enable more engineers to contribute, faster.
DevRamp pinpoints where your team lacks context, strategically allocating reviews to augment context distribution that delivers long-lasting productivity gains.
Broaden contributor coverage
Strengthen team flexibility
Accelerate onboarding speed
Build Expertise
Make critical changes without bottlenecks.
DevRamp identifies the review opportunities that build deep context or diminish existing context, ensuring you have multiple engineers capable of making complex changes.
Deepen critical expertise
Reduce review bottlenecks
Preserve system mastery
On average organizations achieve a 2x improvement in context distribution
within the first 90 days by simply following DevRamp's reviewer
recommendations.
DevRamp can tell you exactly what your improvements would have been over
the past 90 days.
Request Audit
End Key Person Risk
Avoid silos and reduce the impact of turnover.
DevRamp identifies code that relies on a single engineers context and leverages every review as an opportunity to distribute siloed context without adding unrealistic toil.
Break expertise silos
Mitigate attrition risk
Mitigate attrition risk
Better AI Context
Extract maximum value from AI.
By ensuring engineers have the necessary code context, engineers prompt AI more effectively and can critically evaluate AI outputs more confidently.
Improve prompt quality
Validate AI completions
Avoid context errors
Manage Code Risk
Explore risk areas across the codebase.
DevRamp pinpoints active areas of the codebase where team members have low or no context. These blind spots are the highest-risk zones for bugs, regressions etc.
Identify fragile code
Prioritize risky areas
Guide safe decisions
Frequently Asked Questions
What is Capacity and why is it important?
Capacity measures whether your team has enough code context to remain productive across the codebase in most situations. A high Capacity score means your organization can handle a wide range of tasks without relying on a small group of individuals.
A 100% Capacity score means that for every part of your codebase, five or more people in your team are familiar enough with the code to be productive.
DevRamp recommends an Active Code Capacity score of 80% or higher to ensure resilience, flexibility, and efficient collaboration.
A 100% Capacity score means that for every part of your codebase, five or more people in your team are familiar enough with the code to be productive.
DevRamp recommends an Active Code Capacity score of 80% or higher to ensure resilience, flexibility, and efficient collaboration.
What is Expertise and why is it important?
Expertise measures whether your team has the depth of code context required to tackle complex coding tasks and issues.
A 100% Expertise score means that for every part of your codebase, three or more people in your team have the expertise needed to solve challenging problems effectively.
DevRamp recommends an Active Code Expertise score of 80% or higher to reduce bottlenecks and key-person risk on complex work.
A 100% Expertise score means that for every part of your codebase, three or more people in your team have the expertise needed to solve challenging problems effectively.
DevRamp recommends an Active Code Expertise score of 80% or higher to reduce bottlenecks and key-person risk on complex work.
Is our code or data used to train models?
No. Your source code and metadata never leave our infrastructure and are never used to train any machine learning models.
How is our data stored?
DevRamp encrypts all data at rest and in transit. Source code is analyzed in-place and never persisted outside your environment. For customers using our private cloud or on-premise deployments, all data remains fully contained.
Can we deploy DevRamp on-premise?
Yes. Our Scale plan supports on-premise or private cloud deployments. Contact us to discuss your requirements.
Ready to make AI work for your team?
DevRamp builds context augmentation into your development process, helping
your engineers and AI deliver faster, safer and better software.
Get Started
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