Production AI Applications on the OpenAI API
We use GPT-4o for fast, cost-effective responses and the o-series models for complex reasoning — routing between them to optimise quality and cost for every task.
How we use OpenAI API
The OpenAI API gives access to GPT-4o (fast, multimodal, excellent for customer-facing applications) and the o-series reasoning models (exceptional for multi-step analysis, code, and math). We build production applications that route intelligently between models — simple queries go to the cheap, fast models; complex reasoning goes to the powerful ones. This keeps costs manageable without sacrificing quality where it counts.
What this means for your project
- ✦Production-ready OpenAI API integrations, not demos
- ✦Code you own — no black boxes
- ✦Engineers who have shipped real systems with this stack
- ✦Ongoing support and updates after launch
What we build with OpenAI API
Customer support AI
GPT-4o handles natural conversation, intent detection, and response generation for support chatbots — fast enough for real-time use and capable enough to handle nuanced requests.
Document analysis
Extract key information from contracts, invoices, and reports using structured output mode — accurate, deterministic, and ready to write to your database.
Complex reasoning tasks
o4-mini handles multi-step analysis, financial modelling assistance, and code debugging where careful step-by-step reasoning is required.
Vision and image analysis
GPT-4o Vision processes images, screenshots, and documents — enabling AI that sees what your customers send, not just what they type.
Function calling and agents
Build agents that call your APIs, update your database, and take real-world actions using OpenAI's function calling interface.
Common questions
Do you use OpenAI or Anthropic for client projects?+
Both — we choose based on the task. Claude is better for long documents and reliable instruction-following; GPT-4o is better for speed and multimodal inputs; o-series for reasoning. Most production systems use more than one model.
How do you manage costs on the OpenAI API at scale?+
We use prompt caching, model routing (cheap models for simple tasks), output length control, and batching where possible. A well-architected system typically costs 10× less than a naive implementation at the same quality level.
Related technologies
Want to build with OpenAI API?
Tell us what you are building — we scope it for free and reply within 24 hours with a plan and fixed price.
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