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More than an API key in a config file
Calling the OpenAI API for the first time takes a few minutes. Running it reliably inside a product used by real people is a different job. You need prompts that don’t drift when someone edits them, outputs your code can parse, limits on how much any one user can spend, sensible behaviour when the API is slow or unavailable, and a record of what was sent and returned.
Our OpenAI integration work covers all of that. The model becomes one well-behaved component of your system rather than a black box sitting in the middle of it.
Where businesses use it
- A SaaS product adding a “summarise this”, “draft a reply” or “explain this report” button for its users.
- An e-commerce team classifying incoming products into the right categories and attributes.
- A real estate portal turning structured listing data into readable descriptions for review.
- A clinic or diagnostics chain using an internal tool to turn staff notes into structured summaries, with a person checking every one.
- An operations team searching years of internal documents in plain language using embeddings.
What we build into every integration
- Versioned prompt templates stored in your codebase, reviewed like any other code, so a change can be tested and rolled back.
- Structured outputs — responses constrained to a JSON schema so your application receives predictable fields.
- Function calling (tool use) where the model needs to look up data or trigger an action in your system, with your code deciding what is actually allowed.
- Rate limiting, retries and timeouts that handle API errors gracefully instead of freezing your interface.
- Token budgets and per-user or per-account cost caps, so a runaway loop or a heavy user can’t run up costs unnoticed.
- Usage logging by feature and customer, so you can see where the spend actually goes.
- Streaming responses where users are waiting on screen, so text appears as it is generated.
Matching the model to the task
OpenAI offers several model tiers, and they change regularly. Larger models are better at complex reasoning; smaller ones are faster and much cheaper, and often perfectly good at classification, extraction and short drafting. We test your actual tasks on more than one model and pick the smallest that meets the quality bar. Where work doesn’t need an instant answer, the Batch API can process large jobs at lower cost.
Because model names and prices change, we keep the model choice in configuration rather than scattered through the code, so switching later is a small change.
Our delivery process
- Define the feature — what goes in, what should come out, and what a good answer looks like.
- Build an evaluation set — real examples with expected results, used to compare prompts and models.
- Prototype and measure — try prompts and models against the set and review results with you.
- Engineer for production — add the limits, logging, error handling and security around the chosen approach.
- Launch gradually — to internal users or a small group first, watching quality and cost.
- Hand over — documentation of prompts, configuration, limits and how to monitor usage.
What affects timeline and cost
- Complexity of the feature — a single summarise button is quicker than a multi-step assistant with tool use.
- Your existing codebase — its language, structure and how easily new services can be added.
- Quality bar — high-stakes outputs need larger evaluation sets and human review steps.
- Expected usage — which shapes caching, batching and model choice, and therefore your ongoing API bill.
- Data handling requirements for personal or confidential information.
Pitfalls we help you avoid
- API keys in the frontend. Keys belong on your server; exposing them in a browser or mobile app invites abuse.
- Prompts pasted into a dashboard that nobody can trace or roll back.
- Trusting output blindly — parsing free text with fragile string matching, or letting model output run as code or database queries.
- Ignoring prompt injection — user-supplied text can try to override your instructions, so permissions must be enforced in your code, not in the prompt.
- No spend alerts. Set limits in your own system as well as on the provider’s account.
Frequently asked questions
Which GPT model should we use?
The smallest one that reliably does your task. We test your real examples on more than one model and recommend based on quality, speed and cost. Model lineups change often, so we keep the choice easy to switch.
Does OpenAI train on our API data?
OpenAI states that data sent through its business API is not used to train its models by default. Policies can change, so we recommend reviewing OpenAI’s current data usage terms, and we design integrations to send only what each task needs.
How do you stop costs from getting out of hand?
Through token limits per request, per-user and per-account caps in your own system, caching where answers repeat, the right model per task, and usage logs with alerts. We also recommend spending limits on the OpenAI account itself.
Can you integrate with our existing app?
Usually yes. We work with common backend stacks such as Node.js, Python, PHP and others, and add the AI features as a service your existing code calls.
What happens if the OpenAI API is down or slow?
Every call has a timeout and a retry policy, and your application shows a sensible message instead of freezing. For important features we can add a fallback, such as queuing the request for later or switching to a second provider, so one outage doesn’t stop your product.
Talk to us about openai integration
GPT models wired directly into your product or internal tools, with usage limits and cost controls in place.