Articles grouped by business problem, industry, and system type.
AI can return catalog parts that match a customer's wording as candidates for review. If two variants have different connection types and the description does not say which one is needed, the system should preserve that uncertainty, ask for the missing detail and hand the choice to sales or a product specialist.
A custom model for spend classification is worth considering when supplier rules and your current procurement system confuse different types of purchases. Before training begins, agree on the category structure and check whether the available descriptions distinguish those categories. Choose a solution based on tests with unseen line items and the work required to review its results.
A scratch on a painted housing can stand out in one photo and almost disappear when the light changes. Before paying for a custom inspection model, look at what your images actually show and how many different parts they represent. Adapting a model is worth considering when the defect is visible and simpler image checks still fall short.
If a system repeatedly assigns readable freight document details to the wrong fields, fine-tuning may be worth evaluating. First identify whether the cause is image quality, text recognition, field meaning, or missing information, then test any improvement on documents reserved for evaluation.
Fine-tuning may help route support tickets when a general model repeatedly confuses company-specific categories. First establish whether employees interpret those categories consistently and whether historical records reflect the decision you want the system to make.