Models used where they earn their keep, with limits around them
AI is a component, not a product. It is worth using where the alternative is a human doing something repetitive, and worth avoiding where a deterministic rule is cheaper and never wrong. The interesting engineering is in the boundary between the two.
On-device detection and OCR
Inference that runs on the machine rather than in a data centre: no round trip, no per-call cost, and it keeps working when the connection does not. We have shipped detection and OCR under 100 ms on a custom board.
LLM features with human review
The model proposes, a person approves, and the approval is part of the workflow rather than a policy nobody follows. Generated text that nobody reads is worse than no text.
Generated content, edited before publishing
Product descriptions and copy drafted from the spec sheet, then edited. Hundreds of items become tractable without the catalogue reading like a machine wrote it, because a person still did.
Deterministic limits around the model
Ordinary code holds the boundaries: what the model may spend, what it may change, and what a human must confirm. The model is never the last checkpoint before something irreversible.
Catalogues too large to write by hand
Hundreds of products with no descriptions is a real cost. It is also the case where drafting plus editing beats both writing from scratch and publishing raw output.
Products that must work without a network
Retail floors, vehicles, industrial sites. If the cloud is optional, inference has to happen locally, which changes the model you can use and how it is built.
Anyone unsure whether AI belongs here at all
Sometimes the honest answer is that a rule would do it better and cheaper. We would rather say that before you commit than after.
Start with the scope conversation. It costs nothing and it is usually one call.