Building it for production, not just a demo
Calling the GPT API is easy; running it reliably is the real work. Production integrations need retries and timeouts, streaming for responsive UIs, structured outputs your code can trust, and graceful handling when the model or the network misbehaves.
We build the layer that turns a raw API into a dependable feature: predictable outputs, sane fallbacks, and logging you can actually debug from.
- Streaming responses for a fast, responsive feel
- Structured / JSON outputs validated before your app uses them
- Retries, timeouts, and fallbacks for API hiccups
- Function/tool calling wired to your real backend actions
- Clear logging without storing sensitive user content you don't need
Keeping it secure and cost-conscious
Your API key should never sit in front-end code, and prompts should never leak data users can't see. We keep keys server-side, scope what data is sent, and add per-user and per-day limits so a bug or abuse can't run up a surprise bill.
Cost control is design, not an afterthought: right-sizing the model to the task, caching where it's safe, and trimming prompt bloat all lower the bill without hurting quality.
- API keys kept server-side, never exposed to the browser
- Model chosen to fit the task — not the most expensive by default
- Caching and prompt trimming to cut token cost
- Per-user and per-day usage caps to prevent bill surprises
- Data-handling rules so only what's needed is sent to the API
More on ai integrations
Frequently asked questions
Is it safe to send our data to OpenAI?
It can be, with the right controls: sending only what the task needs, keeping keys server-side, and reviewing OpenAI's data-use terms for API traffic. We help you set boundaries on what leaves your systems and flag anything sensitive before it does.
How do we avoid a runaway API bill?
We add usage caps per user and per day, pick a model sized to the task, and cut wasted tokens with caching and tighter prompts. Costs become predictable instead of a mystery line item.
Should we use GPT or another model?
Depends on the workload — some tasks suit GPT, others suit Claude or an open model. We keep the integration provider-swappable so you can compare on quality and cost rather than being locked in.