What an AI integration actually involves
Calling an API and printing the response takes an afternoon. Making that call reliable at 10,000 requests a day takes longer — and that is usually where teams get stuck.
We have wired LLM providers into CRMs, ERP modules, support desks, and customer-facing search. The work is less about the API call itself and more about what surrounds it: retries when a provider times out, caching for repeated queries, guardrails so a user cannot burn through your monthly token budget in an hour, and logs your team can actually read when something goes wrong at 2 AM.
How we structure the integration
Most projects start with a thin service layer that sits between your application and whichever providers you use. Your product code talks to one internal interface; that interface handles provider selection, authentication, and error translation. If you switch from GPT-4o to Claude for a specific workflow, you change configuration — not every call site in the codebase.
For chat-style features we implement streaming so users see tokens arrive in real time rather than staring at a spinner for eight seconds. For batch or background jobs we queue requests and process them with concurrency limits so a spike in usage does not trip provider rate caps.
Security and cost control
API keys never ship to the browser. All model calls route through your backend or a dedicated edge function. We set per-user or per-tenant quotas, alert you when spend crosses a threshold, and log enough context to debug bad outputs without storing full conversation history unless you explicitly want that.
If you operate in India, Singapore, or the EU, data residency and retention rules matter. We map which provider regions and endpoints fit your compliance requirements before writing integration code.