For Engineering teams using OpenAI in production

OpenAI GPT integration that survives traffic spikes.

Function calling, JSON mode, structured output, vision, prompt caching, retry/fallback chains. The pieces that turn a GPT demo into a production feature.

Get a quotefrom $6,000 · USD

What's included

Production-grade OpenAI GPT integration that ships, not theater.

  • GPT-4o, o1, o3 tier routing
  • Function calling with Zod/Pydantic validation
  • JSON mode + structured output schemas
  • Vision: screenshots, PDFs, images
  • Prompt caching for cost reduction
  • Retries, fallbacks, rate-limit handling

What you walk away with

Deliverables you keep — code, infrastructure, and the runbook.

  • Production OpenAI integration
  • Eval suite + monitoring
  • Cost dashboard
  • Provider abstraction for multi-vendor support

Frequently asked

How do you handle OpenAI rate limits and outages?+

Exponential backoff with jitter, request budgets per user, and fallback chains (GPT-4o → GPT-4 → Claude). Your product stays up when OpenAI has an incident.

When should I use o1 / o3 reasoning models?+

For tasks where the model genuinely needs to think — math, complex code, multi-step planning. They're slower and pricier; I use them when eval shows quality jump justifies cost.

Vision models — practical use cases you've shipped?+

OCR + structured extraction (NameGood does this with business cards). Document processing. UI bug-detection. Product photo analysis. Better than dedicated OCR for most messy real-world images.

Ready to scope your OpenAI GPT integration?

Email me what you're building. I'll respond with a quote, scope questions, and a clear next step.