Ship your AI-powered app with production-grade reliability
Code review and production services for AI wrapper apps, LLM integrations, and ChatGPT-powered applications built with AI coding tools.
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 we solve
The most common issues in AI-built ai wrapper / llm app projects.
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 review
Our ai wrapper / llm app audit covers these critical areas.
API key security — LLM API keys stored server-side, never exposed to client
Cost controls — per-user limits, token counting, usage monitoring
Streaming implementation — proper SSE/streaming for LLM responses
Error handling — timeouts, rate limits, model errors, malformed output
Prompt injection protection — input sanitization, output validation
Rate limiting — user-level and application-level limits
Caching — caching identical or similar requests to reduce costs
Monitoring — cost tracking, latency tracking, error rates
Services for your ai wrapper / llm app
Security Review
Deep security analysis of your application — from API endpoints to database access.
Performance
Identify and fix performance bottlenecks — slow page loads, laggy interactions, and expensive operations.
Add Features
New functionality, integrations, and capabilities that your AI tool couldn't build or that you need built properly.
Infrastructure
Databases, APIs, auth systems, email, file storage — the backend services that power your application.
We work with ai wrapper / llm app apps built with
Start with a self-serve audit
Get a professional review of your ai wrapper / llm app at a fixed price.
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 do I keep API costs under control?
Three strategies: 1) Set per-user usage limits to prevent abuse. 2) Cache responses for identical or similar prompts. 3) Optimize prompts to use fewer tokens — shorter system prompts, focused user prompts, and appropriate model selection (use a cheaper model for simple tasks).
How do I handle prompt injection?
Sanitize user input before including it in prompts. Use structured prompts that separate instructions from user content. Validate output before displaying it. Set up monitoring to detect unusual behavior. There's no perfect solution — it's about layered defenses.
Should I use OpenAI or Anthropic?
Both are excellent. OpenAI (GPT-4) has a larger ecosystem and more integrations. Anthropic (Claude) often produces better results for complex reasoning and longer contexts. Many production apps use both — routing different tasks to different models based on cost and quality.
Other use cases
SaaS App
Professional code review, security hardening, and deployment services for SaaS applications built with AI coding tools.
Marketplace
Code review and production services for two-sided marketplaces built with AI tools.
E-Commerce Store
Production services for e-commerce applications built with AI tools.
MVP / Prototype
Bridge the gap between AI-generated prototype and production-ready MVP.
Building a ai wrapper / llm app?
Tell us about your project. We'll respond within 24 hours with a clear plan and fixed quote.