Build AI-powered apps
with OpenAI
We integrate OpenAI, GPT-4o, Assistants API, embeddings, and vision, into your web and mobile apps. AI chatbots, document analysis, smart search, content generation, and more. Built fast, built right.
The best tool for
ai / llm integration
OpenAI provides the world's most powerful AI APIs, GPT-4o for language, Whisper for voice, DALL·E for images, and embeddings for semantic search. We integrate these capabilities into your product seamlessly.
What we build with OpenAI
From MVPs to enterprise platforms, here's how we use OpenAI to ship faster.
AI Chatbots
Context-aware customer support bots, internal knowledge assistants, and copilots, powered by GPT-4o.
Document Intelligence
Automatic data extraction, summarization, and analysis from PDFs, contracts, and reports.
Semantic Search
Vector-based search using OpenAI embeddings, find the right result even with imperfect queries.
Content Generation
Auto-generated product descriptions, emails, social posts, and reports at scale.
Certified OpenAI experts
We don't just use OpenAI, we master it. Our team is certified and has shipped dozens of projects with it.
Apps delivered
We've shipped over 50 production apps using OpenAI and the broader no-code stack, from seed-stage MVPs to enterprise platforms.
Faster delivery
OpenAI lets us build in weeks what traditional dev teams take months to deliver, giving you a decisive speed advantage.
Fixed pricing
Every project comes with a clear scope, fixed price, and weekly demos. No surprises, no scope creep, just results.
Tools we combine with OpenAI
We integrate OpenAI with the best tools in the no-code ecosystem for end-to-end solutions.
The Complete Guide to OpenAI Development
OpenAI's API gives developers and no-code builders access to the world's most capable language models, enabling AI chatbots, document intelligence, semantic search, and content generation in any application.
OpenAI API vs ChatGPT: What You're Actually Using
ChatGPT is a consumer product, a polished chat interface built by OpenAI that anyone can use at chat.openai.com. The OpenAI API is a programmatic interface that lets developers send requests to OpenAI's models and receive responses in their own applications. When you integrate AI into your SaaS product, you are using the API, not ChatGPT. The distinction matters because the API gives you control over every parameter: the model, the system prompt, the temperature, the output format, and the context window. The API is accessed via HTTP requests to OpenAI's endpoints, authenticated with an API key. You send a messages array containing a system message (instructions for the AI's behaviour) and one or more user messages (the input). OpenAI returns a completion, the model's response. This same request-response pattern powers everything from a simple text summariser to a complex multi-turn conversational agent. The interface is simple; the complexity lies in what you put in the system prompt and how you structure the conversation history. Understanding this distinction also clarifies the billing model. ChatGPT subscriptions ($20/month for Plus) give individual users access to GPT-4 in the chat interface. API usage is billed per token, roughly per word, with no subscription. The costs are unrelated. Your API bill depends entirely on how many tokens your application sends and receives, which models you use, and how many users are making requests. For most early-stage SaaS products, the OpenAI API bill is surprisingly small, often under $50/month in the first year, because individual interactions are cheap at the token level.
Choosing the Right Model: GPT-4o vs GPT-4 Turbo vs GPT-3.5
OpenAI's model family offers a spectrum of capability and cost. GPT-4o is the current flagship: it is fast, highly capable, and supports text, images, and audio in a single model. It handles complex reasoning, nuanced instructions, and long-context documents well. GPT-4 Turbo is the previous flagship, offering similar reasoning capability with a very large context window. GPT-3.5 Turbo is the budget option: significantly cheaper and faster, but noticeably weaker on complex tasks, long documents, and instruction-following. For most production SaaS integrations, the right default is GPT-4o. It costs more than GPT-3.5 but produces meaningfully better outputs for anything involving reasoning, document understanding, or nuanced instruction-following. The cost difference is often irrelevant at early-stage volumes, if you are processing 1,000 user requests per month, the difference between GPT-3.5 and GPT-4o might be $5 versus $50. Once you scale to hundreds of thousands of requests per month, a hybrid strategy makes sense: use GPT-4o for complex tasks and GPT-3.5 for simple classification, keyword extraction, or short-form generation. The context window, how many tokens the model can consider in a single request, is a critical selection factor for document processing use cases. GPT-4o and GPT-4 Turbo support very large context windows, making them appropriate for processing long documents, legal contracts, or lengthy conversation histories. GPT-3.5 has a smaller context window that can truncate long inputs in ways that degrade output quality. Always test your specific use case with real production-like inputs before committing to a model, benchmark outputs and costs together, not separately.
Prompt Engineering for Business Applications
Prompt engineering is the practice of crafting system prompts and user message templates that reliably produce the outputs your application needs. For business applications, reliability is more important than creativity, you need the model to consistently return structured data, follow specific formats, apply consistent judgement criteria, and avoid making things up. Achieving this requires systematic prompt design, not trial and error with single examples. The most effective pattern for business prompt engineering is to give the model a role, a task, constraints, and examples. The role establishes the model's perspective and expertise: 'You are a legal contract analyst with expertise in SaaS vendor agreements.' The task specifies what to produce: 'Extract the payment terms, limitation of liability clause, and termination conditions from the contract below.' The constraints define what not to do: 'Only extract information explicitly stated in the contract. If a term is not present, return null for that field.' Examples (few-shot prompting) show the desired output format with concrete samples. JSON output mode is the most important production feature for business integrations. By instructing the model to return structured JSON and enabling OpenAI's response_format parameter, you guarantee parseable machine-readable output rather than prose that you then need to parse with fragile regex. Define your JSON schema in the system prompt with field names, types, and descriptions. Test with edge cases: documents that are missing fields, ambiguous language, or non-standard formats. Robust prompt engineering means your extraction is accurate when everything is normal and gracefully handles exceptions when it is not.
Managing API Costs: Caching, Batching, and Model Routing
OpenAI API costs scale linearly with token usage, every input and output token costs money. The three most effective levers for controlling costs are caching identical requests, batching non-urgent workloads, and routing requests to cheaper models when the task does not require maximum capability. Implementing all three can reduce your OpenAI bill by 60-80% at scale without degrading user-facing quality. Caching is the highest-ROI optimisation for applications where users ask similar questions or process similar documents. If your application summarises product descriptions, and ten customers ask about the same product, you should compute the summary once, store it in your database, and serve the cached version for subsequent requests. Use a hash of the input text as the cache key. Cache hit rates of 20-40% are common in production SaaS applications, and each cache hit costs zero tokens. Implement caching in your Supabase or Xano backend before you worry about model selection or prompt optimisation. OpenAI's Batch API is designed for non-time-sensitive workloads, document processing, bulk content generation, dataset annotation. Batch requests are processed asynchronously within 24 hours and cost 50% less than synchronous API calls. If your application processes uploaded documents in the background rather than in real time, the Batch API halves your processing costs with no change to output quality. Model routing means automatically selecting GPT-3.5 for simple tasks (is this email spam? categorise this support ticket) and GPT-4o only for tasks that genuinely require advanced reasoning. A two-line classifier that routes requests is worth implementing once your monthly token spend exceeds $200.
Streaming Responses for Better UX in No-Code Apps
OpenAI's API supports streaming, sending the model's response token by token as it is generated rather than waiting for the full response to complete. The difference in user experience is significant: a non-streaming response feels like the application is frozen for 2-10 seconds before suddenly displaying a wall of text. A streaming response feels instant, the user sees words appearing as the model writes, like watching someone type in real time. For any user-facing AI feature, streaming is the expected behaviour. Implementing streaming in a no-code stack typically requires a small backend function. In a Supabase Edge Function or Xano custom endpoint, you make the OpenAI API call with stream: true and forward the server-sent event stream to the frontend client. WeWeb and FlutterFlow can consume server-sent event streams and update the UI incrementally. The implementation is more complex than a standard request-response, but the UX improvement justifies it for any feature where users wait for AI output, chat interfaces, document summaries, content generation forms. For Make-based integrations where streaming is not applicable (you are processing data in a background scenario rather than responding to a user in real time), streaming is not relevant. The streaming consideration is specific to user-facing features with synchronous response expectations. For asynchronous AI processing, nightly document analysis, batch email generation, background data enrichment, use standard synchronous API calls and focus optimisation effort on caching and model routing instead.
Rate Limits, Error Handling, and Production Best Practices
OpenAI's API enforces rate limits at two levels: requests per minute (RPM) and tokens per minute (TPM). New accounts start at low limits and are raised automatically as you spend. Hitting a rate limit returns a 429 Too Many Requests error. In production, your integration must handle these gracefully: implement exponential backoff with jitter, retry the request after a delay, and surface a user-friendly message if the retry budget is exhausted. Never let a raw 429 propagate to the user as an uncaught error. The other common error is context length exceeded, your request contains more tokens than the model's context window allows. This typically happens when users paste very long documents or when conversation history accumulates over many turns. Implement a token counting function (OpenAI's tiktoken library) before sending requests, and truncate or summarise older context when approaching the limit. For document processing, chunk documents into segments that fit within the context window and process them sequentially or in parallel. For production API key management, never expose your OpenAI API key in client-side code, a frontend JavaScript application, a mobile app binary, or a public GitHub repository. Always route OpenAI calls through a server-side function: a Supabase Edge Function, a Xano endpoint, or a dedicated backend service. The server function receives the user's request, adds the API key from a secure environment variable, calls OpenAI, and returns the response to the frontend. This architecture also gives you a natural place to add per-user rate limiting, cost attribution, logging, and content filtering before shipping to production.
Ready to build with OpenAI?
Book a free 30-minute call. We'll scope your project, answer your questions, and send you a fixed quote, no commitment required.
Book a free call →