AI API Integration
Production-grade connections between your existing software and the AI providers that fit your use case.
AI API integration is the practical work of connecting an AI provider — OpenAI, Google Gemini, Anthropic Claude, DeepSeek — to your existing software stack so it does something useful in production, not just in a notebook. That means error handling, retry logic, rate limiting, logging, fallbacks, and cost controls — the same engineering discipline as any other production dependency.
We choose the provider after understanding the use case. A customer-facing chatbot, a background document extraction job, and a real-time recommendation engine each have different latency, cost and accuracy requirements — the right choice for one is rarely right for another.
Integration typically touches three components: an API client in your codebase wired to the provider, a schema addition to store AI-generated fields in your existing database, and a review step so your team can inspect output before it's fully trusted. None of it requires rewriting the rest of your application.
What production integration includes
Provider Selection
The right API for your specific use case — chosen after understanding the problem, not before.
Error Handling & Fallbacks
What happens when the provider times out or returns an unusable response.
Cost Controls
Token limits, request batching, and caching so API costs don't scale unexpectedly.
Audit Logging
Every AI call logged — input, output, provider, latency — so you can trace anything.