Built with Sourcegraph Cody?
Let's make sure it's production-ready.
An AI coding assistant with full codebase awareness powered by Sourcegraph's code graph, enabling accurate suggestions based on cross-repository context and understanding of how code is actually used across your entire organization. We help non-technical founders identify and fix the issues AI tools leave behind.
Common issues we find in Sourcegraph Cody code
These are real problems we see in Sourcegraph Cody projects during our audits — not hypotheticals.
Suggestions based on deprecated or low-quality code patterns found in the existing codebase
Cody's suggestions are grounded in your actual codebase, which means if the codebase contains outdated patterns, deprecated library usage, or known-bad code, Cody will suggest those same patterns in new code — amplifying technical debt.
Cross-repo context can leak patterns from one team's code into another team's service
In large organizations where Cody indexes multiple repositories, suggestions can carry patterns from one team's codebase into another, introducing unfamiliar dependencies, different error handling conventions, or architectural approaches that do not belong in the target service.
Security vulnerabilities in existing code recommended as reference implementations
If the indexed codebase contains known security issues that have not yet been patched — unparameterized queries, missing auth checks, insecure deserialization — Cody may suggest these patterns as examples when generating similar code.
Performance anti-patterns replicated from existing high-traffic code paths
When Cody finds existing code as a reference for a new feature, it may replicate performance issues — N+1 queries, missing indexes, blocking synchronous calls — that are accepted in the existing code but will be problematic at the scale of the new feature.
Test patterns replicated without coverage for error conditions that the existing tests miss
Cody's test generation follows the patterns in your existing test suite. If the existing tests have systematic gaps — no testing of API error responses, no testing of empty states — the generated tests will have the same gaps.
Cross-repository import suggestions that create unintended inter-service dependencies
When indexing multiple services, Cody may suggest importing a utility function directly from another service's repository rather than the shared library package, creating a tight coupling between services that should be independently deployable.
Context window management can exclude relevant files from suggestions in very large repos
Even with Sourcegraph's code graph, extremely large repositories may not have all relevant context loaded during a suggestion, causing Cody to miss established patterns or recently added utilities and regenerate code that already exists.
Generated code may use internal Go or Java conventions not documented externally
In organizations with unique internal conventions (custom error types, proprietary logging formats, internal RPC patterns), Cody's suggestions may partially apply conventions but miss subtle details that are not explicitly documented in the codebase.
How we can help with your Sourcegraph Cody 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 Sourcegraph Cody app to production
Refactor Code
Clean up AI-generated or legacy code
Performance
Make your Sourcegraph Cody app faster and more efficient
Add Features
New functionality, integrations, capabilities
Testing
Add tests and improve coverage
Infrastructure
Set up and manage your Sourcegraph Cody backend
Start with a self-serve audit
Get a professional review of your Sourcegraph Cody 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 Sourcegraph Cody's codebase awareness differ from GitHub Copilot?
Copilot's context is limited to open files in your editor. Cody uses Sourcegraph's code graph to understand your entire codebase — how functions are called, how types are used, and how code is connected across files and repositories. This makes Cody's suggestions more accurate for large codebases where the relevant context may not be in the current file.
Can we use Cody with self-hosted Sourcegraph to keep code on our own infrastructure?
Yes — Cody works with both Sourcegraph Cloud and self-hosted Sourcegraph Enterprise. Self-hosted deployment keeps your code graph and queries within your own infrastructure, which is important for organizations with strict data residency or air-gap requirements. Enterprise pricing is required for self-hosted deployments.
How do we prevent Cody from learning from and replicating technical debt in our codebase?
The most effective approach is proactively improving the codebase quality — Cody will suggest better patterns as the reference code improves. In the short term, you can scope Cody's context to specific directories or repositories that contain higher-quality code. Adding explicit code comments marking deprecated patterns also helps Cody understand what not to replicate.
What programming languages does Cody support best?
Cody performs best with TypeScript, Go, Python, and Java — languages with large open-source corpora that the underlying models are trained on, and where Sourcegraph's code graph is most mature. Support for languages like Rust, C++, and PHP is available but the suggestion quality is lower. Check Sourcegraph's documentation for the current language quality tier list.
Does Cody work well for a 10-person startup or is it more suited to enterprise teams?
Cody's codebase-awareness advantage grows with codebase size. For a 10-person startup with a smaller codebase, general-purpose tools like Copilot or Cursor may provide comparable results at lower cost. Cody becomes more valuable as the codebase reaches 100K+ lines and cross-file context starts to matter for accurate suggestions. The free tier is available to evaluate without commitment.
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