Built a ai wrapper / llm app with Augment Code?
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 Augment Code apps
Building a ai wrapper / llm app with Augment Code 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 Augment Code ai wrapper / llm app
Common Augment Code issues we fix
Beyond ai wrapper / llm app-specific issues, these are Augment Code patterns we commonly fix.
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.
Start with a self-serve audit
Get a professional review of your Augment Code 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 Augment Code?
Augment Code 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 Augment Code leave in ai wrapper / llm apps?
Common issues include: cross-module dependency suggestions that violate architectural boundaries, security patterns from older parts of the codebase propagated to new code, suggestions mirror existing buggy patterns rather than fixing the root cause. For a ai wrapper / llm app specifically, these issues are compounded by the need for api cost management.
How do I make my Augment Code ai wrapper / llm app production-ready?
Start with our code audit ($19) to get a clear picture of what needs fixing. For most Augment Code-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 Augment Code-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 Augment Code ai wrapper / llm app production-ready
Tell us about your project. We'll respond within 24 hours with a clear plan and fixed quote.