Want to add AI to your product but don't know how to connect it to your data?
Built a ChatGPT integration that works in demo but hallucinates in production?
ChatGPT Integration Services
ChatGPT is a product. The OpenAI API is the infrastructure behind it. What most businesses need is not ChatGPT -- they need GPT-4o or GPT-4 Turbo integrated into their specific application, trained on their data, and delivering outputs their users can act on.
We integrate the OpenAI API into your existing web app, mobile app, or internal tool -- adding AI capabilities grounded in your data, constrained to your use case, and working reliably in your production environment.
OpenAI API integration: GPT-4o, GPT-4 Turbo, GPT-4o mini
RAG pipelines connecting the model to your knowledge base and documents
Function calling for tool use and structured data extraction
Streaming responses, cost management, and production monitoring
RaftLabs integrates OpenAI's GPT-4o, GPT-4 Turbo, and GPT-4o mini APIs into existing applications and custom products. We handle the full integration stack -- prompt engineering, RAG pipeline for grounding responses in your data, function calling for tool use and structured output, streaming response handling, cost management, and production monitoring. We've shipped 20+ AI-powered products using the OpenAI API.
Integration that works in production, not just in the demo
Most ChatGPT/OpenAI integrations that fail in production share a common pattern: the team connected the API, wrote a system prompt, and shipped. No data grounding. No output validation. No cost monitoring. No handling for when the model does not know the answer.
We build the full integration -- not just the API call.
What we integrate
AI-powered chat and assistants
Conversational AI features within your application: customer support assistants, onboarding guides, knowledge base Q&A, and internal company assistants. Grounded in your product documentation, knowledge base, or business data via RAG. Streaming responses for real-time feel. Conversation history management within context window limits. Escalation paths to human agents when confidence is low.
Document and content AI
AI features that work on documents: summarisation of long reports, extraction of specific data from contracts and forms, generation of first drafts from structured data, and translation of technical content into plain language. Handles PDFs, Word documents, and plain text. Chunking and context management for documents longer than the model's context window.
AI for your product
Adding AI capabilities to SaaS products: AI writing assistance, smart autocomplete, content generation from templates, AI-powered recommendations, and intelligent data categorisation. Integrated into your existing product UI and data model. Prompt engineering calibrated to your product's voice and user expectations.
Structured data extraction
Using function calling to extract structured data from unstructured inputs: parsing user messages into CRM-ready fields, extracting key terms from documents into database records, classifying incoming requests into categories with supporting evidence. JSON output validated against your schema before writing to your system.
AI agents and tool use
Agents that use tools to complete multi-step tasks: an AI that can look up an order, check inventory, apply a discount, and send a confirmation email in response to a customer message. Function calling connects the model to your APIs. State management tracks the agent's progress. Error handling manages tool failures gracefully. See our multi-agent systems page for complex multi-agent orchestration.
Cost and performance optimisation
Reducing OpenAI API costs for existing integrations: right-sizing model selection per task (GPT-4o mini where GPT-4o is overkill), response caching for repeated queries, prompt compression to reduce input tokens, and output length control. Production monitoring for cost per conversation, latency distribution, and error rates. Cost optimisation typically reduces production API spend by 30--60% without quality degradation.
Tell us what AI feature you want to add.
The application, the user problem you're solving, and the data you want the model to work with. We'll scope the integration and give you a fixed cost.
Related services
Generative AI Integration -- broader generative AI integration beyond OpenAI
Claude Integration -- Anthropic Claude API integration
Gemini Integration -- Google Gemini API integration
RAG Pipeline Development -- knowledge grounding infrastructure
LLM Fine-Tuning -- custom model training for specific tasks
Frequently asked questions
ChatGPT is OpenAI's consumer product -- a chat interface anyone can use at chat.openai.com. The OpenAI API is the programmatic interface that lets you integrate GPT-4o and other models into your own applications. When businesses say they want to 'integrate ChatGPT', they mean they want OpenAI API integration -- the same underlying models, but integrated into their specific product, workflow, or data environment with custom prompts, data connections, and output formats.
GPT-4o: the flagship model, best for complex reasoning, analysis, and nuanced tasks. Higher cost per token. GPT-4o mini: significantly cheaper, surprisingly capable on focused tasks -- the right choice for high-volume production use cases where cost compounds. GPT-4 Turbo: large context window (128K tokens), good for long document analysis. o1 and o3 reasoning models: for tasks requiring multi-step logical reasoning. We recommend the right model for each specific task -- not the most expensive one as default.
Retrieval-augmented generation (RAG). Your documents, product knowledge, or database content are indexed into a vector store (Pinecone, Weaviate, or pgvector in PostgreSQL). When a user asks a question, we retrieve the relevant content from your index and include it in the model's context. The model answers based on your specific data rather than general training knowledge. This prevents hallucination on company-specific topics and grounds responses in accurate, current information.
OpenAI function calling lets the model trigger specific actions or return structured data rather than free-form text. Use cases: returning structured JSON for your application to process (extract specific fields from a user message), triggering actions in your system (creating a support ticket, looking up an order, updating a CRM record), and building AI agents that use tools to accomplish multi-step tasks. Function calling is how you make AI integrations that do things, not just say things.
Hallucination prevention strategy: RAG grounds responses in your actual data. System prompts constrain the model to answer only from provided context. Confidence handling -- prompting the model to say when it does not know rather than guess. Output validation for structured outputs (checking that returned JSON matches expected schema). Human-in-the-loop review for high-stakes outputs. Monitoring and logging for hallucination patterns identified in production. No approach eliminates hallucination entirely -- the goal is making it detectable and handleable.
Integration development costs $20,000--$80,000 depending on complexity -- a single AI feature in an existing application runs less; a full AI-powered product with RAG, function calling, and multiple AI workflows runs more. Ongoing OpenAI API costs scale with usage -- GPT-4o at $5/1M input tokens and $15/1M output tokens, GPT-4o mini at $0.15/$0.60 per 1M tokens. We model the expected monthly API cost at your estimated volume before committing to the build.