OpenAI & GPT integration
We integrate OpenAI's models into products and workflows for US companies: assistants, function calling, RAG, structured outputs, and fine-tuning, built to be reliable and cost-aware in production rather than impressive in a demo.
No sales script. You talk to the engineers who'd build it.
Our team works a shifted day so you get real-time standups and same-day turnarounds across US time zones, not next-morning replies.
Every line of code, model weight, and prompt is yours from day one. NDAs and clean IP assignment are standard, not an upsell.
You work directly with the engineers building your system. No account managers sitting between you and the people writing code.
We move from scoping to a working system in production in weeks. Most engagements ship something usable inside the first month.
What we build
Concrete systems we ship, tuned to your data and your stack.
Assistants & functions
GPT wired to your tools through function calling so it completes real tasks.
RAG on your data
Ground GPT in your private content so answers are accurate and cited.
Fine-tuning
Tune models for your domain when prompting alone isn't enough.
Cost control
Caching, batching, and model routing to keep spend sane at scale.
How we work
Scope & evals
We pin down what success means and build the evaluation set before writing the feature, so quality is measured, not guessed.
Build in the open
Weekly demos against real data. You see progress every week and can change direction before it gets expensive.
Ship & instrument
We deploy with logging, cost tracking, and guardrails in place, then tune against production traffic.
Hand off or stay
Take the keys with full docs, or keep us on for iteration. Either way you're never locked in.
Questions, answered
Should we use GPT, Claude, or both?
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Depends on the task. We benchmark on your actual use case and often use more than one model, routing each request to whichever performs best for the cost. We're provider-agnostic.
When is fine-tuning worth it?
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When you need consistent formatting, a specific style, or better accuracy on a narrow task that prompting can't reach. For most use cases, good prompting plus RAG gets there first and cheaper.
How do you keep GPT costs under control?
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We track token usage per feature, cache where we can, and send easy requests to smaller models. Cost is a design constraint from the start, not a surprise on the invoice.
Can you build with the Assistants and Responses APIs?
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Yes. We work across OpenAI's current APIs and pick the right one for your latency, tooling, and control needs.
Let's scope your build.
Tell us what you're trying to ship. We'll tell you honestly whether AI is the right tool and what it would take.
Start the conversation