OpenAI API Development Services

We start by reviewing your current architecture and defining exactly which OpenAI capabilities you need, text generation, embeddings, vision, structured outputs, or the Assistants API. Integration is typically a two-to-four week sprint: we wire the API into your backend with proper error handling, retry logic, and cost controls, then build the UI layer on top. Most SaaS products need prompt management, streaming support, and token usage tracking, we include all three as standard.

API costs scale with token volume, so we build cost controls from the start: caching repeated prompts with semantic similarity matching, batching low-priority requests, setting per-user and per-session token budgets, and routing simpler tasks to cheaper models (GPT-4o mini or GPT-3.5) while reserving GPT-4o or reasoning models for tasks that need them. We instrument every API call so you can see spend by feature, user, and model in a dashboard.

Retrieval-Augmented Generation (RAG) pulls relevant documents from your knowledge base at query time and sends them as context to the model. Use RAG when your data changes frequently, when you need source citations, or when the knowledge base is too large to fine-tune on. Fine-tuning is better when you need consistent output style, domain-specific jargon, or faster inference on a narrow task. In practice, we use RAG for most enterprise use cases because it is easier to update and debug.

We build an evaluation suite early in the project: a set of representative prompts with expected outputs, scored automatically using embedding similarity and LLM-as-judge techniques. This runs in CI on every prompt change, so regressions are caught before deployment. For customer-facing applications, we also add human review queues for low-confidence outputs and feedback loops that feed back into prompt improvement over time.

A focused integration, one AI feature, clearly defined inputs and outputs, typically takes two to four weeks from scoping to production. A full AI-native product (chat interface, document processing pipeline, or recommendation engine) is typically eight to sixteen weeks depending on data complexity and integration requirements. We fix the scope and cost before starting, so you know what you are getting and when.