AI features built on the OpenAI API require more than a chat completion call.
Production AI applications need streaming responses, token cost management, prompt versioning, error handling for rate limits and failures, and the user experience patterns that make AI features feel reliable. We build OpenAI integrations for production applications.
Application that needs to integrate OpenAI — for summarization, generation, classification, or chat — and needs it built for production reliability, not just a proof of concept
Adding an OpenAI API call to a prototype is straightforward. Building a production AI feature is harder:
Streaming vs. blocking responses. The default OpenAI API call returns the full response when generation is complete. For long responses, users wait 10–30 seconds staring at a spinner. Streaming with stream: true and the Vercel AI SDK returns tokens as they're generated — the same experience ChatGPT provides. Implementing streaming correctly requires server-sent events or WebSockets and the React patterns to display partial responses.
Cost management. GPT-4o costs $5 per million input tokens and $15 per million output tokens. An application that generates 10,000 user queries per month with large prompts can easily cost thousands of dollars per month. Token budgeting, prompt optimization, and using the right model for each task (GPT-4o-mini for simple tasks, GPT-4o for complex ones) are production requirements.
Rate limit handling. The OpenAI API has per-minute token limits that vary by tier. Production applications need exponential backoff retry logic and rate limit error handling that degrades gracefully.
Prompt management. Prompts that are hardcoded in application code are hard to iterate. Production AI applications need prompt versioning, the ability to A/B test prompts, and monitoring of prompt performance.
Retrieval-Augmented Generation. AI features that need to answer questions about your specific data (documents, knowledge base, product catalog) require RAG: embedding the data in a vector database and retrieving relevant context at query time.
OpenAI API integration with streaming, rate limit handling, cost monitoring, and the UX patterns that make AI features feel reliable to users
Streaming API routes
Next.js Route Handlers with the Vercel AI SDK's `streamText` or `streamObject`. Server-sent event streaming to the React client. Loading states and partial response display.
Cost monitoring
Token usage logging per request. Model selection logic (cheap model for simple tasks, expensive model for complex). Usage dashboards for cost visibility.
Rate limit handling
Retry with exponential backoff using `openai-node`'s built-in retry configuration. Rate limit error handling with user-friendly degradation.
Structured output
JSON mode or `response_format: { type: 'json_object' }` for AI features that need structured data. Zod schema validation on AI-generated JSON.
RAG implementation
Document embedding pipeline, vector storage (Postgres pgvector or Pinecone), and retrieval at query time. Relevant context injection into the prompt.
One honest number to start.
Fixed-scope, fixed-price. The number below is the starting point — final scope is built from your brief.
OpenAI API integration with streaming, rate limit handling, cost monitoring, and the UX patterns that make AI features feel reliable to users
Three steps, every time.
The same repeatable engagement on every project. No surprises, no mystery, no billable ambiguity.
Brief & discovery.
We send you questions, then get on a call. Output: a written scope with every step, feature, and integration listed.
Build & ship.
Fixed schedule, weekly reviews. No scope creep unless you change the scope — and if you do, we reprice it transparently.
Warranty & retainer.
30-day warranty on every launch. Most clients stay on a monthly retainer for ongoing features and maintenance.
Why Fixed-Price Matters Here
AI feature scope is defined by the use case, the data sources, and the UX requirements. Fixed price.
Questions, answered.
Use GPT-4o-mini for: summarization, classification, simple generation tasks, high-volume low-complexity use cases. Use GPT-4o for: complex reasoning, code generation, tasks where output quality matters significantly. The cost difference is approximately 25×. Using GPT-4o for every task when GPT-4o-mini would suffice is expensive.
AI reliability requires: output validation (Zod schema for structured outputs), confidence estimation for classification tasks, human review workflows for high-stakes outputs, and feedback mechanisms to log and review bad outputs.
AI features are part of the application build. Full application with AI features from $28k. Fixed-price.
Tell Ryel about your project.
Describe what you’re building and what outcome you need. You’ll have a written, fixed-price scope within the week.