AI Integration Services

Each leads on different tasks and prices move often, so we recommend integrating through a thin abstraction and choosing per feature by testing on your data. Claude is strong on long documents and agents, OpenAI on breadth and Realtime voice, Gemini on multimodal and Google Cloud fit. Most clients end up using two.

Caching, smaller models for simple steps, prompt trimming, batch APIs for offline work, per-tenant limits and a spend dashboard. We estimate cost during design and set alerts so surprises are caught early rather than on the invoice. Cost per feature and per tenant is visible in a dashboard, so you can price and limit usage with real numbers rather than estimates.

Yes. We work in Python, Node and TypeScript primarily and adapt to your framework and conventions. Integrations are delivered as reviewed pull requests, services or SDKs, with tests and documentation, so your engineers own them afterwards. We start with a short architecture review so integrations follow your patterns for auth, logging and deployment instead of introducing a parallel stack.

Keys in a secrets manager, a gateway that logs and redacts PII, no-training data agreements or private endpoints such as Bedrock and Azure OpenAI, and audit trails. We document the data flow so security reviews are quick. Where an enterprise customer requires it, we can route their traffic through a dedicated regional endpoint or a private model without changing application code.

We build with retries, back-off, queues and provider fallbacks, and test failure modes deliberately. Critical paths degrade gracefully, to a cached answer or a human route, instead of failing silently. Load tests against provider limits are part of delivery, so you know your real ceiling before a launch or campaign finds it for you.