OpenAI

We use OpenAI's models across the range of AI features clients need: copilots and chat assistants, content generation, summarisation, classification and extraction, semantic search with embeddings, and tool-using agents that take real actions. GPT's function calling and structured output make it especially good at turning messy language into reliable, structured results your systems can act on.

The key is that a model call is only a small part of a real feature. We build the surrounding machinery — retrieval over your data, prompt design, validation, fallback handling — that turns a capable model into a dependable product, rather than a clever demo that breaks on the third edge case.

Frontier models are powerful but non-deterministic, so we treat quality as something to measure, not assume. We build evaluation suites that score the model's outputs against real cases, so changes to prompts or models are validated with evidence rather than vibes — and regressions get caught before users see them.

We ground responses in your actual data with retrieval (RAG) so answers are accurate and grounded rather than invented, and we add guardrails — input and output validation, topic boundaries, and human-escalation paths — so the feature stays on-brand and safe. That engineering discipline is what separates a production AI feature from a proof of concept.

Model choice is an engineering trade-off between quality, speed, and cost, and it changes fast as new models ship. We architect AI features behind a clean abstraction so you can switch models — a cheaper one for simple calls, a more capable one for hard tasks, or a different provider entirely — without rewriting your product.

That portability protects you. Because we don't hard-wire your product to one vendor's API, you keep leverage on pricing and can adopt better models as they arrive. OpenAI is often the right choice today; the architecture ensures it doesn't have to be forever.