OpenAI Developers Who Take GPT Prototypes to Production

Wiring a first call to the OpenAI API takes an afternoon. What clients ask Cortance to fix is everything after it: a support bot that invents a refund rule, a nightly job that burns through the token budget, an agent that calls a function nobody meant it to call. The brief we get is rarely "connect the API." It is "make this hold up once customers use it," and that is the skill we screen OpenAI developers for.

Which parts of a GPT integration fail under customer traffic?

Most failures we see sit around the model, not inside it. Rate limits return 429 errors under load, and code without retries and backoff drops requests silently. Tool calls return JSON the backend never validated, so a malformed argument reaches a database write. Retrieval pulls a document from the wrong customer because nobody filtered the vector search by tenant. When we vet a candidate, we ask them to walk through one of these from their own work: what failed and what they changed. Demo-only developers usually have nothing to walk through.

A platform deadline also sits behind many current requests. OpenAI removed the Assistants API on August 26, 2026, one year after announcing its deprecation, and directed developers to the Responses and Conversations APIs. Teams using thread-and-run logic on the old API need to move that state handling without disrupting live conversations. At least three engineers in our network configured Assistants API threads, messages, and tools for a production booking system, so we can match the migration work with someone who knows the old model from the inside.

How two of our engineers fenced in what the model can do

Yaroslav R., a .NET developer, built the AI assistant layer for a flight-booking system. Because the assistant could act on bookings, he limited it to an allow-listed tool layer: the model could only trigger pre-approved operations, every argument was checked against JSON Schema DTOs, and each AI-initiated booking action went into an audit log with its inputs and outcome. He worked with both OpenAI GPT-4 and Azure OpenAI on that project, which helps when a company has not settled on one platform yet.

Oleh Sh., a senior TypeScript and Python AI engineer with four years of OpenAI work, designed the agent workflow for a CRM and lead-operations platform that scores leads, parses intent and routes them. The agent stops after each routing decision and writes a structured recommendation to a queue for a person to approve. Pydantic schemas check the output at every step, and an immutable log records who approved what. A sales team can audit that kind of automation instead of switching it off after the first wrong email.

Pick the orchestration layer after the use case, not before

About a third of the 22 OpenAI projects in our engineers' history also use LangChain, often alongside LlamaIndex, LangGraph or Pinecone. A framework earns its place when the job needs multi-step agents, document indexing or switching between model providers. For a single feature that returns structured data, the SDK with function calling is often easier to test and cheaper to maintain. A developer who reaches for an agent framework to classify support tickets will cost you more in maintenance than the build saved.

What we let AI coding assistants write on OpenAI projects

Claude Code appears on three of the OpenAI projects in that history. It speeds up the repetitive parts: typed clients for each function schema, test fixtures, prompt variants to compare. It does not decide which operations a model may trigger, how long conversation data is kept, or when a low-confidence answer goes to a person. Those calls stay with a senior engineer, and they are what our interviews test.

Send us the feature you are building, the data it touches and where it runs today. We will match OpenAI developers who have shipped something close to it, and the engineer you choose can start within 2 days. Our screening is described in the guide on vetting generative AI developers.