Built a ai wrapper / llm app with Sweep AI?
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 Sweep AI apps
Building a ai wrapper / llm app with Sweep AI 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 Sweep AI ai wrapper / llm app
Common Sweep AI issues we fix
Beyond ai wrapper / llm app-specific issues, these are Sweep AI patterns we commonly fix.
PRs may be too narrow, missing related bug sources
Sweep fixes the specific symptom described in the GitHub issue but often misses related root causes in adjacent code. The bug reappears from a different trigger after the narrow fix.
Security changes in PRs not reviewed for regression
When a GitHub issue involves auth or permissions, Sweep's generated PR modifies security-sensitive code. These changes require careful human review that automated PR creation workflows can bypass.
Generated tests verify implementation, not behavior
Sweep writes tests alongside its code changes, but the tests assert that the specific implementation works rather than that the feature behaves correctly across realistic input scenarios.
Edge cases not covered when issue description is vague
Sweep implements what the issue says literally. Vague issue descriptions lead to implementations that miss important edge cases — null inputs, concurrent requests, or invalid data.
Start with a self-serve audit
Get a professional review of your Sweep AI 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 Sweep AI?
Sweep AI 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 Sweep AI leave in ai wrapper / llm apps?
Common issues include: prs may be too narrow, missing related bug sources, security changes in prs not reviewed for regression, generated tests verify implementation, not behavior. For a ai wrapper / llm app specifically, these issues are compounded by the need for api cost management.
How do I make my Sweep AI ai wrapper / llm app production-ready?
Start with our code audit ($19) to get a clear picture of what needs fixing. For most Sweep AI-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 Sweep AI-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 Sweep AI ai wrapper / llm app production-ready
Tell us about your project. We'll respond within 24 hours with a clear plan and fixed quote.