Built with GitHub Copilot?
Let's make sure it's production-ready.
GitHub Copilot provides inline code suggestions and completions within existing IDEs. Unlike agent-based tools, it works within your existing codebase but can introduce inconsistencies and security issues through individual completions. We help non-technical founders identify and fix the issues AI tools leave behind.
Common issues we find in GitHub Copilot code
These are real problems we see in GitHub Copilot projects during our audits - not hypotheticals.
Insecure code patterns from training data
Copilot sometimes suggests patterns from its training data that are known to be insecure - like using eval(), innerHTML, or outdated crypto functions.
Hardcoded secrets in suggestions
Copilot occasionally suggests placeholder API keys or credentials that look real and get committed to version control.
Inconsistent code style across files
Different completions use different patterns - sometimes callbacks, sometimes async/await, sometimes .then(). The codebase becomes inconsistent over time.
Subtly incorrect logic
Copilot completions often look correct but contain off-by-one errors, wrong comparison operators, or missed edge cases that cause intermittent bugs.
Deprecated API usage
Copilot suggests code using deprecated APIs or outdated library versions from its training data, introducing compatibility and security issues.
Unnecessary dependencies
Copilot suggests importing libraries for operations that could be done with native APIs, increasing bundle size and attack surface.
No deployment awareness
Copilot completes code in isolation without understanding the deployment context. Production-specific concerns like env vars and build optimization are missed.
Generated tests with poor assertions
Copilot-generated tests often have weak assertions (checking that a function 'doesn't throw' rather than checking actual output), providing false confidence.
How we can help with your GitHub Copilot project
From security reviews to deployment, we cover everything you need to go from prototype to production.
Security Review
Deep security analysis and hardening
Fix Bugs
Resolve issues and unexpected behavior
Deploy & Ship
Get your GitHub Copilot app to production
Refactor Code
Clean up AI-generated or legacy code
Performance
Make your GitHub Copilot app faster and more efficient
Add Features
New functionality, integrations, capabilities
Testing
Add tests and improve coverage
Infrastructure
Set up and manage your GitHub Copilot backend
Platform Migration
Move off AI platforms to your GitHub Copilot own infra
Start with a self-serve audit
Get a professional review of your GitHub Copilot project at a fixed price. Results reviewed by experienced engineers.
Security Review
Automated Security Scan
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Manual Security Review
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Full Pentest
Enterprise-grade engagement tailored to your needs. Dedicated engineer, ongoing support.
Fix Bugs
Code Audit
AI-powered analysis of your codebase. Get a detailed report with prioritized findings within 24 hours.
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Bug Fixing
Expert engineer works on your project directly. Fixed scope, fixed price, no surprises.
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Ongoing Support
Enterprise-grade engagement tailored to your needs. Dedicated engineer, ongoing support.
Refactor Code
Code Audit
AI-powered analysis of your codebase. Get a detailed report with prioritized findings within 24 hours.
Get StartedRefactor Code
Refactoring
Expert engineer works on your project directly. Fixed scope, fixed price, no surprises.
Get a QuoteRefactor Code
Full Rewrite
Enterprise-grade engagement tailored to your needs. Dedicated engineer, ongoing support.
100% of your audit purchase is credited toward any paid service. Start with an audit, then let us fix what we find.
How it works
Tell us about your app
Share your project details and what you need help with.
Expert + AI audit
A human expert assisted by AI reviews your code within 24 hours.
Launch with confidence
We fix what needs fixing and stick around to help.
Frequently asked questions
Is Copilot-assisted code less secure?
Research shows Copilot can introduce security vulnerabilities through insecure patterns from training data. A security review is especially important for Copilot-heavy codebases.
Can you audit a codebase that used Copilot extensively?
Yes. We look for the specific patterns Copilot introduces - inconsistent error handling, insecure suggestions, and deprecated API usage throughout the codebase.
How do I make my Copilot code production-ready?
Focus on consistency, security review, and testing. We standardize patterns across the codebase, fix security issues, and add test coverage for critical paths.
Is Copilot better or worse than agent-based AI tools?
Different risks. Copilot gives you more control but introduces inconsistencies. Agent-based tools produce more consistent code but with less oversight. Both need review.
Should I stop using Copilot?
No - Copilot is a great productivity tool. Just treat its suggestions as starting points that need review, not finished code. Pair it with periodic code audits.
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