Built with Augment Code?
Let's make sure it's production-ready.
An AI coding assistant designed for large codebases and monorepos, providing suggestions with full codebase context awareness. Works across TypeScript, Python, Java, and Go projects and understands cross-module dependencies. We help non-technical founders identify and fix the issues AI tools leave behind.
Common issues we find in Augment Code code
These are real problems we see in Augment Code projects during our audits — not hypotheticals.
Cross-module dependency suggestions that violate architectural boundaries
Augment Code's broad codebase awareness can lead it to suggest importing modules or calling functions across architectural layers that should be decoupled — for example, suggesting a UI component directly call a database utility instead of going through a service layer.
Security patterns from older parts of the codebase propagated to new code
When the codebase contains legacy code with deprecated or insecure patterns, Augment Code learns from those patterns and may suggest them in new code, spreading outdated authentication, validation, or encryption approaches.
Suggestions mirror existing buggy patterns rather than fixing the root cause
If the codebase has a consistent bug pattern — such as a missing null check in multiple similar functions — Augment Code will replicate that bug in new suggestions because it learns from existing code rather than reasoning about correctness.
Monorepo package boundary confusion leads to circular import suggestions
In large monorepos with shared packages, Augment Code sometimes suggests imports that create circular dependencies between packages, which can cause build failures or subtle runtime initialization order bugs.
Performance-intensive patterns replicated across services without optimization
If the codebase has established patterns for data fetching or processing that work at small scale but not at high load, Augment Code will replicate these patterns, potentially scaling performance problems across new services.
Test suggestions follow existing test patterns including gaps in coverage
Augment Code generates tests that mimic existing test files, including any systematic gaps — for example, if the existing tests never test error paths, new generated tests will also omit error-path coverage.
Build configuration not updated when new dependencies are added by suggestions
Augment Code may suggest using packages that are installed in one part of a monorepo but not another, causing build failures in packages that received the suggestion but do not have the dependency in their own package.json.
Inconsistent naming conventions adopted from different parts of large codebases
In codebases with mixed naming conventions across services (often the case in monorepos that have grown over years), Augment Code may introduce the naming convention from one service into another, creating inconsistencies.
How we can help with your Augment Code 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 Augment Code app to production
Refactor Code
Clean up AI-generated or legacy code
Performance
Make your Augment Code app faster and more efficient
Add Features
New functionality, integrations, capabilities
Testing
Add tests and improve coverage
Infrastructure
Set up and manage your Augment Code backend
Start with a self-serve audit
Get a professional review of your Augment Code 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
How does Augment Code handle very large monorepos with millions of lines of code?
Augment Code is specifically designed for large monorepos and uses indexing to maintain codebase context without loading everything into the context window at once. Performance stays consistent even in very large repos, though initial indexing can take time. Teams at companies with 10M+ line codebases use it effectively.
Does Augment Code send our proprietary source code to external servers?
Augment Code indexes your codebase locally and sends relevant code snippets as context with queries. Review their enterprise data handling agreement carefully — enterprise plans typically include data processing agreements that restrict how code is used for model training. For highly sensitive codebases, confirm this with their sales team before adopting.
Why does Augment Code sometimes suggest patterns from a different service in our monorepo?
Augment Code's codebase awareness is broad by design — it searches across the whole repo for relevant patterns. When it finds a similar pattern in another service, it may replicate it even if that service has different requirements. You can mitigate this by adding context in your prompt about which service or layer you are working in.
How should we configure Augment Code to respect our architectural boundaries?
Use Augment Code's configuration to specify which directories or packages are in scope for a given editing session. Adding architectural documentation and comments in key boundary files (like index.ts barrel exports) helps the tool understand where boundaries lie. Some teams add explicit README notes in package directories describing what should and should not be imported.
Is Augment Code worth the cost for a 5-person startup versus GitHub Copilot?
Augment Code's main advantage is codebase-wide context — Copilot's context window is limited to open files. For small teams with simple project structures, Copilot is often sufficient. Augment Code's value becomes clearer once your codebase has multiple interconnected modules where understanding cross-file dependencies matters for getting accurate suggestions.
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