OpenAI API and ChatGPT integration developers who build custom AI features, RAG systems, and LLM automation into your product, not just API wrappers.
Senior OpenAI API developers with RAG, prompt engineering, and LLM evaluation experience
Flexible models, part-time, full-time, or dedicated team
Start in days, not weeks
In short
RaftLabs provides ChatGPT and OpenAI API developers for hire who build custom GPT integrations, AI-powered product features, RAG pipelines, and LLM-based automation into your existing stack. Available part-time, full-time, or as a dedicated team with full source code ownership. Engagements start within days. The team has shipped production LLM systems for SaaS companies, enterprises automating document workflows, and product teams adding AI capabilities to existing applications.
Recognition
Tried the ChatGPT API but outputs are not reliable enough for your production use case?
Need developers who understand RAG, embeddings, and LLM evaluation, not just API call wrappers?
We work with product teams and companies that need LLM expertise to build AI features that are reliable enough for production.
Adding an AI feature to an existing product is harder than it looks. The API call is the easy part. The hard parts are prompt stability under diverse inputs, retrieval quality, latency, cost, and evaluation. We handle all of it, shipping AI features that your users trust.
Contracts, invoices, reports, and compliance documents contain information that humans currently extract manually. We build LLM pipelines that read these documents, extract structured data, and route it into your existing systems with the accuracy level your operations team can rely on.
Feature-level AI integrations: AI-assisted writing, semantic search, smart categorisation, AI-generated summaries, and conversational product interfaces. We build these as proper product features with error handling, fallbacks, and the ability to swap the underlying model when a better one ships.
We provide OpenAI API and LLM development resource for client projects, embedded in your delivery process, following your standards, and available at the capacity your project needs.
ChatGPT integrated into your product as a feature, not a demo. We design the system prompt, handle conversation history, manage token budgets, and build the UX layer so the AI feature behaves predictably for your users and does not surprise them with hallucinated content.
Retrieval-Augmented Generation connects your documents, knowledge base, or database to the LLM so it answers questions about your data accurately. We build the full pipeline: document chunking, embedding generation, vector store setup, retrieval logic, and the LLM call that synthesises the answer.
Workflows that previously required human judgement, automated with LLMs. Document classification, data extraction from unstructured text, email triage, ticket categorisation, and content generation pipelines. We build these with evals so you know when accuracy degrades.
LLM-based extraction from PDFs, contracts, invoices, and compliance documents. We combine structured output modes, multi-pass prompting, and validation logic to hit the accuracy thresholds your operations workflows require, not just close-enough demo accuracy.
Conversational interfaces built on the ChatGPT API for customer support, internal knowledge bases, product onboarding, and lead qualification. We design the conversation architecture, tool calling logic, and handoff to humans when the AI reaches its limits.
Production AI features need to stay reliable as inputs vary and models update. We build evaluation datasets, automated test suites, and monitoring that catches accuracy regressions before your users do.
Senior OpenAI API engineers, flexible engagement, full source code ownership.
Prompt engineering is not writing clever instructions. It is systematic design of system prompts, few-shot examples, and output format specifications that produce reliable results across the full range of inputs your users will send.
We have built RAG systems using Pinecone, pgvector, Weaviate, and Qdrant. The retrieval step determines answer quality more than the LLM call does. We tune chunking strategy, embedding models, and retrieval ranking to hit the quality bar your use case requires.
Part-time for a single AI feature, full-time for an AI product build, or a dedicated team for a larger programme. We match the model to your current project phase.
Senior LLM engineering talent without local senior developer costs. No recruitment timeline, no benefits overhead, no notice period when you need to adjust the team.
We build LLM integration layers that abstract the model, so switching from GPT-4o to Claude or Gemini is a configuration change, not a rewrite. Your AI features stay current as better models ship.
LLM API costs compound fast at production scale. We design token-efficient prompts, add caching for repeated queries, and use smaller models where the task does not require GPT-4-class capability. The result is an AI feature your unit economics can support.
Tell us what AI feature you want to build, the data it needs to work with, and the accuracy level you need in production. We'll match you with the right LLM engineers.
A 30-minute call to understand your existing product, the AI use case, and whether ChatGPT is the right tool for the job or whether a different approach would work better.
A clear proposal with team composition, timeline, and cost, before any commitment.
Engineers onboard in days. First sprint planned. AI feature in development within the first week.
Pricing
Dedicated senior engineers from $6,000/month (minimum pod $12,000/month).
Senior OpenAI API engineers available in days. Fixed cost or monthly retainer. Full source code ownership.
We work with OpenAI, Anthropic's Claude API, Google's Gemini API, and open-source models via Ollama or hosted inference providers like Together AI and Fireworks. For most product use cases, GPT-4o is a sensible default, but we recommend the model based on the task, cost constraints, and latency requirements rather than defaulting to the most expensive option.
A direct ChatGPT integration uses the model's training data to answer questions. It works well for general tasks but cannot answer questions about your specific data, documents, or internal knowledge. A RAG pipeline adds a retrieval step that fetches relevant content from your data before the LLM generates an answer. The result is an AI feature that can accurately answer questions about your business, your products, and your internal documentation.
We build evaluation datasets that represent the range of inputs your users will send, and we run evals before and after every change to the prompt or retrieval logic. For high-stakes use cases like document extraction or compliance workflows, we add validation layers that catch structured output errors and fall back to human review rather than surfacing wrong answers to users.
Yes. LLM integration is most useful when the model has access to your actual data. We build function calling and tool use patterns that let the LLM query your database, call your internal APIs, and write back to your systems when the task requires it. The integration layer is designed so the LLM operates within defined boundaries and cannot trigger unintended side effects.