Part of AI Integration
OpenAI API integration
GPT, embeddings, Realtime and vision APIs integrated into your product with streaming, function calling, caching and cost controls.
In one paragraph
OpenAI API integration: GPT, embeddings, Realtime and vision APIs integrated into your product with streaming, function calling, caching and cost controls. AI integration services connect models from OpenAI, Anthropic Claude, Google Gemini and open-source providers into your existing software: SaaS products, backends, mobile apps and internal tools. We build the API orchestration, middleware, SDKs and pipelines that make AI features reliable, secure and cost-controlled, for product and engineering teams that want AI in production, not in a demo.
How openai api integration fits into ai integration
Ship an AI feature in your SaaS
Add drafting, summarisation or Q&A to your product with proper tenancy, limits and billing.
Consolidate AI usage
Put every team behind one gateway with keys, logging, redaction and spend visibility.
Multi-provider resilience
Route between OpenAI, Claude and Gemini with fallbacks so outages and price changes do not hurt you.
Process at volume
Embed, classify or generate over millions of records with queues and batch APIs.
How we deliver
- 1
Architecture review
Understand your stack, data flows, security requirements and where AI calls will live.
- 2
Integration design
Choose providers, define contracts, error handling, streaming, caching and cost limits.
- 3
Build and test
Implement services, SDKs or middleware with tests, evaluation cases and load testing.
- 4
Observability and rollout
Tracing, cost dashboards and feature flags, then staged release to users.
Frequently asked
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.
Related work
Outcomes, not demos.
Ready to chart your AI course?
A free 30-minute consultation: we listen, ask hard questions and tell you honestly where AI will and won't help.