Built a ai wrapper / llm app with Sourcegraph Cody?
We'll make it production-ready.
AI wrapper apps — products built on top of OpenAI, Anthropic, or other LLM APIs — have unique production challenges. The AI model is a black box that's slow, expensive, and unpredictable. Your app needs to handle variable response times, API failures, cost management, and output quality control in ways that standard web apps don't.
AI Wrapper / LLM App challenges in Sourcegraph Cody apps
Building a ai wrapper / llm app with Sourcegraph Cody is a great start — but these challenges need attention before launch.
API cost management
LLM API calls are expensive. A single unoptimized prompt can cost cents per request — which adds up fast with real users. You need token counting, cost tracking, usage limits per user, and prompt optimization to stay profitable.
Response time variability
LLM responses take 1-30 seconds depending on prompt complexity, model load, and output length. Your UI needs streaming responses, loading states, and timeout handling. Users abandon apps that feel slow.
Error handling for AI responses
The AI model might: return an error, time out, return malformed output, refuse to answer, or hallucinate. Each case needs specific handling. AI tools build the happy path but not the many failure modes.
Prompt injection and security
Users can manipulate your AI's behavior through carefully crafted inputs — making it ignore instructions, reveal system prompts, or produce harmful output. Input sanitization and output validation are essential.
Rate limiting and queuing
LLM APIs have rate limits. When many users make requests simultaneously, you need a queue system to manage the flow and provide feedback to waiting users. Without this, users get API errors during peak usage.
Output quality control
LLM responses aren't deterministic — the same prompt can produce different quality results. You need output validation, retry logic for poor responses, and potentially human review for critical outputs.
What we check in your Sourcegraph Cody ai wrapper / llm app
Common Sourcegraph Cody issues we fix
Beyond ai wrapper / llm app-specific issues, these are Sourcegraph Cody patterns we commonly fix.
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Cody's suggestions are grounded in your actual codebase, which means if the codebase contains outdated patterns, deprecated library usage, or known-bad code, Cody will suggest those same patterns in new code — amplifying technical debt.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.
Start with a self-serve audit
Get a professional review of your Sourcegraph Cody ai wrapper / llm app at a fixed price.
External Security Scan
Black-box review of your public-facing app. No code access needed.
- OWASP Top 10 vulnerability check
- SSL/TLS configuration analysis
- Security header assessment
- Expert review within 24h
Code Audit
In-depth review of your source code for security, quality, and best practices.
- Security vulnerability analysis
- Code quality review
- Dependency audit
- Architecture review
- Expert + AI code 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.
Frequently asked questions
Can I build a ai wrapper / llm app with Sourcegraph Cody?
Sourcegraph Cody is a great starting point for a ai wrapper / llm app. It handles the initial scaffolding well, but ai wrapper / llm apps have specific requirements — api cost management and response time variability — that need professional attention before launch.
What issues does Sourcegraph Cody leave in ai wrapper / llm apps?
Common issues include: suggestions based on deprecated or low-quality code patterns found in the existing codebase, cross-repo context can leak patterns from one team's code into another team's service, security vulnerabilities in existing code recommended as reference implementations. For a ai wrapper / llm app specifically, these issues are compounded by the need for api cost management.
How do I make my Sourcegraph Cody ai wrapper / llm app production-ready?
Start with our code audit ($19) to get a clear picture of what needs fixing. For most Sourcegraph Cody-built ai wrapper / llm apps, the critical path is: security review, then fixing core flow reliability, then deployment. We provide a fixed quote after the audit.
How much does it cost to fix a Sourcegraph Cody-built ai wrapper / llm app?
Our code audit is $19 and gives you a complete report of issues. Fixes start at $199 with our Fix & Ship plan. For larger ai wrapper / llm app projects, we provide a custom fixed quote after the audit — no hourly billing.
Get your Sourcegraph Cody ai wrapper / llm app production-ready
Tell us about your project. We'll respond within 24 hours with a clear plan and fixed quote.