Images as input
Reading a photographed document, describing a defect in an inspection shot, or checking that an uploaded image is what the form claims it is.
AI / OpenAI
OpenAI's models cover a wide surface — text, images, audio, embeddings, realtime voice — behind one well-documented API with an enormous surrounding community. When a project needs to explore several modalities quickly, that breadth is worth a lot.
Reading a photographed document, describing a defect in an inspection shot, or checking that an uploaded image is what the form claims it is.
Speech in, speech out, low enough latency to feel conversational — useful for hands-busy field work where typing isn't an option.
A dependable, well-understood embedding layer underneath search and RAG, with the index kept portable so switching provider later is a re-index, not a rebuild.
When the goal is to find out fast whether an idea has legs, the shortest path to a working prototype is often here. What ships to production is decided later, by evaluation.
Nothing about using a broad, convenient API justifies relaxing the engineering around it. If anything, convenience is exactly when discipline slips.
Residency, retention and processing terms are a legal question, not a technical one. Settle it first — the answer sometimes rules out hosted inference entirely.
Shared infrastructure has quotas and busy periods. We design the queue, the backoff and the degraded experience before you meet them in production.
Prototype economics are meaningless. We model spend at realistic volume, because that's the number that decides whether the feature survives.
A narrow prototype plus a real evaluation set will tell you more in two weeks than a strategy deck will in two months.