Production systems
built on OpenAI.
OpenAI's embedding models and GPT-class models are a strong default for retrieval and for high-volume, cost-sensitive extraction work — one option in a stack we choose per project, not a default we reach for out of habit.
Serving UK and EU clients: GDPR, EU AI Act, and data residency are covered on our Trust & Safety page.
A default for retrieval,
a workhorse for extraction.
OpenAI's embedding models are a well-tested, widely compatible default for RAG retrieval, and its GPT-class models are a solid fit for structured extraction and high-volume requests where per-call cost matters more than maximum reasoning depth.
We route to it for specific jobs, not as a house default — see our note on avoiding AI vendor lock-in for why we build every integration behind a provider abstraction instead of calling one SDK directly everywhere.
High-volume work,
priced to scale.
The cases where OpenAI tends to be the right call in our stack.
The embedding layer in RAG pipelines, often paired with ChromaDB for the vector store.
Pulling structured fields out of unstructured documents at volume, where a cheaper model tier handles the bulk of the work.
Model routing by document complexity — exactly the pattern OpenAI's pricing tiers make worth building.
Before you
book a call.
The questions we get asked most about building with OpenAI — answered straight, no sales pitch.
Why use OpenAI instead of another provider?
We pick per project. OpenAI's embedding models are a strong, well-tested default for RAG retrieval, and GPT-class models are a solid fit for structured extraction and high-volume, lower-complexity requests where cost per call matters. For heavier reasoning or long-context work we'll often route to a different model instead.
Are you locked into OpenAI once you build on it?
No — we build a thin provider abstraction into the architecture from the start specifically so that isn't true. See our note on avoiding AI vendor lock-in.
What have you actually used OpenAI for?
Embeddings and generation in RAG pipelines, and structured extraction tasks such as pulling risk signals out of documents — the kind of high-volume, cost-sensitive work covered in our LLM inference cost reduction case study.
Tell us what
you're trying to build.
Book a 30-minute call — we'll tell you honestly whether OpenAI is the right model for your project, or whether something else is.