OpenAI integration working in the playground but failing in production?
AI costs out of control because the implementation wasn't designed for scale?
AI engineers who build production systems with OpenAI's APIs -- GPT-4 integration, Assistants API, RAG systems, fine-tuned models, and AI features that work reliably beyond the demo.
OpenAI API engineers with production system and AI product experience
GPT-4o, Assistants API, function calling, embeddings, and DALL-E integration
Start in days. Fixed cost or monthly retainer.
We work with product teams and enterprises building with OpenAI's APIs for real-world applications.
Integrating GPT-4 into an existing SaaS product -- AI writing assistant, smart search, content generation, or automated reporting. We build the integration, evaluation framework, and cost controls that make it production-ready.
Building a product where OpenAI is the core capability. We design the full AI stack -- prompt architecture, RAG system, Assistants API threads, function calling, and backend infrastructure.
Using GPT-4 for document intelligence -- extracting structured data from contracts, invoices, reports, and forms. We build the pipeline from document intake to structured output with validation.
OpenAI API integration that worked in testing but produced inconsistent results in production. We diagnose the architecture, rebuild the prompt design and retrieval system, and add the evaluation framework that was missing.
Production-grade GPT-4o and GPT-4o-mini integration into your product backend. Streaming responses, token budget management, rate limit handling, retry logic with exponential backoff, and cost tracking per user or feature. Integration that handles real load, not just demo traffic.
Thread and assistant management with the Assistants API -- persistent conversation context, file search (RAG with OpenAI's vector store), code interpreter, and custom function calling. We build the backend that manages assistant instances, threads, and runs at scale.
Retrieval-augmented generation using OpenAI's text-embedding-3 models with a vector database (Pinecone, pgvector, or Weaviate). Document chunking, embedding generation, retrieval pipeline, and re-ranking to ensure the right context reaches GPT-4.
Structured tool definitions and function calling integration -- connecting GPT-4 to your APIs, databases, and external services. AI agents that can take actions, not just produce text. Reliable function call parsing with error handling for malformed outputs.
Fine-tuning GPT-4o-mini on your domain data for tasks where prompt engineering alone doesn't produce consistent enough output. Training data preparation, fine-tune job management, evaluation against base model, and deployment to your production integration.
OpenAI costs grow fast without deliberate management. We implement model tiering (GPT-4o for complex tasks, GPT-4o-mini for routine ones), prompt caching, response caching for repeated queries, and token budget controls. Most applications can reduce OpenAI spend by 40--70% with the right architecture.
GPT-4 integration, Assistants API, RAG systems, and production AI architecture.
We've built OpenAI-powered products that serve real users at scale. We know what breaks at 1,000 requests per day and how to fix it before it happens to you.
We set up automated evaluation before we optimise. You can't improve AI reliability without measuring it. Every integration includes a test suite that catches regressions when prompts or models change.
OpenAI spend is a product cost. We design integrations with per-request cost visibility, model tiering, and caching so costs are predictable and controllable at scale.
Evaluation scores, cost per request, token usage, and error rates -- at the cadence you choose. You see the performance of your AI features in numbers.
OpenAI integration is one part of building AI products. Our engineers also handle the backend API, database, authentication, and deployment -- no separate team required.
We build integrations that can switch models -- OpenAI today, Anthropic Claude or a fine-tuned model tomorrow. You're not locked into one provider's pricing when requirements or costs change.
For a specific AI feature, GPT-4 integration, or RAG system build.
For sustained AI feature development or a full AI-powered product build.
A full AI engineering team for complex AI-first products or enterprise AI deployments.
A single GPT-4 powered feature integrated into your existing product -- chatbot, document Q&A, content generation, or extraction pipeline.
An AI-first product with multiple GPT-4 features, RAG system, Assistants API, and production deployment.
Enterprise-scale AI deployments with fine-tuning, compliance requirements, or multi-system integration.
Tell us the use case -- what you want GPT-4 to do, what data it needs, and what system it needs to integrate with.
A 30-minute call to understand the task, the data, and what production reliability means for your use case.
A clear proposal with scope, timeline, and fixed or retainer cost -- including the evaluation framework.
Engineers onboard in days. Evaluation baseline set in week one. Working AI features in production within two weeks.
GPT-4 integration engineers available in days. Fixed cost or monthly retainer. Full source code ownership.
Frequently Asked Questions
We work with the full OpenAI API surface -- GPT-4o and GPT-4o-mini for text generation, text-embedding-3 for embeddings and RAG, Assistants API for stateful multi-turn agents with file search and code interpreter, function calling for tool use, DALL-E for image generation, and Whisper for speech-to-text. Model selection is based on your performance requirements and cost budget, not a default to the most expensive option.
Cost management starts in the architecture design. We implement model tiering -- GPT-4o for complex reasoning tasks, GPT-4o-mini for classification and simple extraction -- which typically reduces costs by 60--80% without quality loss for routine tasks. Prompt caching (supported by OpenAI's API for repeated context), response caching for identical queries, and token budget controls in the API call keep costs predictable. We build cost dashboards so you can see per-request and per-user costs.
The standard Chat Completions API is stateless -- each request is independent, and you manage conversation history by appending messages. The Assistants API manages conversation threads and state server-side, supports persistent file search (built-in RAG with OpenAI's vector store), and includes code interpreter. The Assistants API is better for multi-turn conversational agents with document access. Chat Completions is better for single-turn tasks, streaming responses, and scenarios where you need precise control over context. We choose the right API for your specific use case.
Yes, if we design the integration correctly. We abstract the LLM provider behind an interface so the application logic doesn't know or care which model is running. This makes switching from OpenAI to Anthropic Claude, Google Gemini, or a fine-tuned open-source model a configuration change rather than a rewrite. Most OpenAI integrations built without this abstraction require significant rework to change providers.