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sometimes suggests patterns from its training data that are known to be insecure — like using eval(), innerHTML, or outdated crypto functions.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Copilot suggests code using deprecated APIs or outdated library versions from its training data, introducing compatibility and security issues.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
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 Scan
Black-box review of your public-facing app. No code access needed.
- OWASP Top 10 checks
- SSL/TLS analysis
- Security headers
- Expert review within 24h
Code Audit
In-depth review of your source code for security, quality, and best practices.
- Security vulnerabilities
- Code quality review
- Dependency audit
- AI pattern analysis
Complete Bundle
Both scans in one package with cross-referenced findings.
- Everything in both products
- Cross-referenced findings
- Unified action plan
100% 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.
Get a clear plan
We respond in 24 hours with scope, timeline, and cost.
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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Get your GitHub Copilot app production-ready
Tell us about your project. We'll respond within 24 hours with a clear plan and fixed quote.