AI Product Recognition for Products, Parts and Assets

Identifying which specific product is in an image, rather than just detecting that an object is there. A general vision model can tell you it is looking at a bag of produce. Product recognition tells you it is loose Pink Lady apples, which is the answer a till, a stock count or an audit actually needs.

From the appearance of the item itself: shape, colour, texture, packaging, the things a person uses to tell one product from another. That matters most for the items barcodes never covered, such as loose produce, bakery, anything sold by weight, and anything a customer would otherwise look up on a chart.

Yes, and that is the hardest version of the problem. A loose apple looks different every time, and no barcode or PLU code was ever going to cover it. Recognising produce, bakery and items sold by weight at self-checkout and on scales is what the engine was first proven on, across more than 1,000 retail locations.

Yes. One image, captured in the Tiliter app on a phone or on the web, or from a self-checkout camera, a scale or a fixed camera. The result comes back in the workflow as structured data next to the photo it came from, rather than as a label somebody still has to read and retype.

About 300 milliseconds for a single fresh item, which is quicker than finding the right code on a chart. That is the point at a self-checkout where a customer is standing there waiting. A frame with many items to separate takes longer, up to roughly ten seconds, because the work grows with how much is in the picture.

A general vision API hands back a classification and stops there. Everything that makes it usable is then yours to build: capturing the image in the first place, deciding what to do when the model is unsure, getting the answer into the system that acts on it, and keeping a record that stands up later. That surrounding work is the product here. Capture on web or mobile, a confidence score on every result, uncertain cases routed for review, the answer written into the system where the decision happens, and an auditable trail of all of it.

Yes. The same engine matches parts, tools, components and fixed assets against a catalogue, which is usually harder than retail because the visual difference between two part numbers can be millimetres. See industrial part identification and verification for how that runs in practice, and industrial operations for the wider picture.

Self-checkout and assisted checkout, AI scales for loose and weighed items, and shelf and stock checks that run as scheduled workflows. A team member gets the check on their phone, captures what is in front of them, and the result is recorded against the store and the date. It runs across retail operations in more than 1,000 locations, processing over 50 million images a year.

Often not. Plenty of everyday items are recognised as they are, a bowl of food being one, so you can point it at those and get an answer straight away. A catalogue only comes into it when the answer has to be your SKU rather than a category: telling two own-brand variants apart, or matching a part number where the visual difference is millimetres. And if all you need is how many of something there are, object counting needs no catalogue either.

Every result carries a confidence score, so an uncertain identification can be queued for review rather than quietly becoming the answer. What accuracy you actually get depends on your own range and images, including lighting, camera placement and how alike your products look, which is why you get a score per result instead of one headline number that would not survive contact with your shelves.

First in the workflow that asked for it. The check arrives in the Tiliter app on web or mobile, whoever is capturing sees the result there and then, and it is kept against the job with the photo as evidence, so the answer is reviewable later rather than gone the moment it is read. From there it can be pushed to wherever the decision gets made: the till, the scale, the inventory record, or Salesforce. It also runs on enterprise scanners from Zebra, Honeywell and Datalogic as well as standard cameras.

No, and the distinction matters when scoping a project. Detection finds that something is present. Counting answers how many. Recognition answers which exact item, the hardest of the three and the one checkout and inventory accuracy depend on. Many operations use more than one.