I integrate OpenAI into your product, workflow, or backend — from simple GPT-4 completions to stateful Assistants with file search, code interpreter, and custom function calling. Production-ready, deployed on AWS.
Add GPT-4 chat to your web app, API, or internal tool. Custom system prompts, conversation history, streaming responses, and content moderation — built with FastAPI or Django backend.
Upload your documents (PDFs, CSVs, contracts) to an OpenAI Assistant and let it answer questions from your knowledge base. Ideal for support bots, document Q&A, and research tools.
Connect GPT-4 to real tools — CRM APIs, databases, web search, custom services. The model calls the right function, gets the result, and reasons over it to complete multi-step tasks.
Use GPT-4 to classify emails, tickets, leads, or documents — extract structured data from unstructured text, normalize addresses, parse invoices, and categorize content at scale.
Auto-generate personalized emails, product descriptions, legal summaries, or reports using GPT-4 — with brand voice prompts, variable injection, and output validation to ensure quality.
Deploy OpenAI-powered APIs to AWS Lambda, EC2, or ECS — with rate limiting, cost monitoring, error handling, and async processing via Celery + Redis for high-volume workloads.
GPT-4o is the best default for most applications — fast, multimodal, and highly capable. GPT-4o-mini is ideal for high-volume, cost-sensitive tasks (classification, extraction). The Assistants API (with file search) is best for document Q&A. I recommend the right model based on your latency, cost, and capability requirements.
Yes. I build RAG (Retrieval Augmented Generation) systems using either OpenAI's native file search (simplest) or custom vector stores with pgvector or Pinecone (more control). The system lets GPT-4 answer questions about your specific documents — contracts, knowledge bases, product docs, legal filings — with source citations.
I implement: prompt compression (summarizing conversation history instead of sending full context), model routing (cheap model for simple tasks, expensive model only when needed), response caching (store identical query results), and cost monitoring via usage tracking and budget alerts. Most clients see 40–70% lower API costs vs a naive integration.
Yes — and this is often the most cost-effective approach. n8n has native OpenAI nodes for completions, embeddings, and AI agents. For more complex requirements, I build a FastAPI endpoint that n8n/Make.com calls via webhook — giving you full control over the OpenAI integration with no node limitations.
Book a free 30-minute call. I'll assess your use case and give you a fixed-price quote for the integration.