A structured review of Trigo's retail computer vision platform for loss prevention and autonomous checkout for loss prevention, store operations and retail IT teams: what it does well, where the constraints sit and who should look elsewhere. Every score traces to something Trigo publishes.
Trigo is the most precisely aimed product on this hub, and that is a compliment. It follows an item from the aisle to the checkout to the exit on the cameras a store already has, states plainly that it works with any camera system regardless of brand or specifications, and anchors the whole thing to the transaction by integrating with the store's existing point of sale and self-checkout. The named set is short and legible: hidden products, self-checkout non-scan, checkout skipping, manned checkout monitoring and Scan and Go losses. The privacy posture is published rather than implied, with no biometric identification, automatic face blurring and anonymized shopper tracking, and deployment is stated at as little as two weeks with one in-store workstation. It scores lower everywhere the loss is not at the till, because no safety, operations or perimeter set appears on its pages, and lower again on the second product line, because autonomous retail carries hardware conditions the loss prevention module does not. Choose Trigo when the loss you are chasing happens at the checkout. Look elsewhere when it happens at the stockroom door, the loading bay or at 03:00.
Five criteria, each scored from Trigo's own current documentation as of August 14, 2026. Spot AI competes with Trigo, so no score here rests on an anonymous source, an aggregated user rating or a private benchmark: each one sits next to the documented fact behind it. Anything Trigo does not document is recorded as not publicly specified rather than assumed. Where a fact was not on the loss prevention, self-checkout, autonomous retail or technology pages, this review says so rather than filling the gap from a press summary. This is not a paid placement, and Trigo had no input into it.
The path from the shelf to the exit is documented in detail and runs on the cameras already installed. Everything outside that path is not on a page.
Trigo sells retail computer vision in three shapes: loss prevention across the store and at self-checkout, autonomous retail for frictionless checkout, and retail intelligence for store data. The loss prevention product is the one most buyers arrive for, and its mechanism is simple to state. Trigo watches what a shopper picks up, watches what gets scanned, and reconciles the two, following items from aisle to checkout to exit. Its own framing of the requirement is a connection to the camera network already in the store, a single in-store workstation and integration with the point of sale.
Camera reuse is stated more broadly here than almost anywhere else on this hub. Trigo publishes that it works with any camera system regardless of brand or specifications and that it uses the existing camera network with no additional camera investment required, and it says most deployments need minimal new hardware and can be completed in as little as two weeks. The autonomous retail product is the exception and is honest about it, calling for ceiling-mounted cameras, compact edge computing units and a reliable internet connection, in stores it states it has deployed at up to 1,200 square meters.
The named detections are checkout shaped and legible: hidden products, self-checkout non-scan, checkout skipping, manned checkout monitoring and Scan and Go losses, with mis-scans, fake scans, payment skippers and walkouts named on the self-checkout page. The documented response is a real-time notification to staff and security carrying video evidence, with the option of a prompt on the self-checkout screen showing the shopper the item image. Privacy is published in the same plain way: no biometric identification, automatic face blurring, fully anonymized shopper tracking and compliance stated against GDPR.
Trigo is strongest within a few meters of the till, and thinnest the moment the loss moves to the stockroom door, the loading bay or an hour when nobody is on the floor.
Both columns are documented. Nothing here comes from an anonymous review.
Both columns describe documented behavior. Trigo's column was checked against its own documentation on August 14, 2026.
Swipe the table sideways to see every column.
Trigo data comes from Trigo's own public documentation as checked on August 14, 2026. Gaps are marked as not publicly specified.
The honest split, stated the way a shortlist call would state it.
If the exception report says self-checkout, if non-scans and walkouts are the pattern, and if the point-of-sale data is already clean enough to reconcile against, Trigo is a precise answer to a precise problem and it runs on the cameras already in the ceiling. The two-week deployment figure and the published privacy posture also make it one of the easier products here to get through a works council and a rollout plan.
If the pattern is the stockroom door, the loading bay, an after-hours entry or a parking lot, none of it is in the published set, and the documented response is a notification to somebody who has to be free to act on it. Teams that also want safety and operations answers from the same cameras are looking at two vendors on one estate, which is workable and worth pricing against one platform that covers both.
Spot AI ships 15+ pre-trained Video AI Agents spanning vehicle break-in, fire, intrusion, personal protective equipment, forklift near-miss, falls and hazard-zone crowding, so a mixed estate gets shipped coverage on both sides. Iris builds anything else in natural conversation in about eight minutes, full-resolution video stays on the Intelligent Video Recorder in the building and the platform is SOC 2 Type II, NDAA-compliant and HIPAA-aligned.
These two overlap on one square of the store and diverge everywhere else. Trigo reconciles the aisle against the till and does it on the cameras already installed. Spot AI puts named AI coworkers across the whole estate, security, safety and operations, on the same feeds, keeps full-resolution video in the building and acts at the moment through standard speakers. A team whose loss is entirely at the checkout should hear that Trigo is built for exactly that. A team whose loss moved months ago should hear the opposite.
The cheapest way to settle it is to pick two stores rather than one: the store with the worst self-checkout numbers and the store with the worst back-of-house numbers. Run both platforms for a fortnight and read what each caught in each place. That comparison costs a fortnight rather than a procurement cycle, and it answers the only question that matters here, which is where your loss actually is.
Ask both vendors to quote the identical store list, split into software, in-store hardware, installation and point-of-sale integration work, then to state plainly what is still not covered afterwards.
See what the AI catches on your live feeds before any platform decision.
Customer-reported outcomes from named Spot AI customers.
Blackmon Oil runs a vehicle loitering filter to keep parking lots clean and safe at its around-the-clock stores, with a smaller overnight crew.
Silver Bay Seafoods replaced fragmented legacy camera systems across 22 locations, including remote Alaska facilities, and lifted operational efficiency 15%.
Unique Industries covers more than a million square feet with a three-person safety team, catching near misses and falls on the cameras already installed.
"The biggest benefit of Spot AI is how easy it is to look up incidents, see the footage, and then share it and collaborate."
Yes, for the right problem. It scores 3.6 out of 5 here, carried by a broad camera-reuse statement, a point-of-sale anchored detection set and a published privacy posture. It fits a chain whose loss sits at the checkout and whose transaction data is clean enough to reconcile against. Retailers whose loss has moved to the back of house or after hours should read the published detection list before anything else.
Yes, and its wording is unusually broad. Trigo states that it works with any camera system regardless of brand or specifications and that it uses the existing camera network with no additional camera investment required, with a single in-store workstation and integration with the point of sale. Its autonomous retail product is the exception and states its own requirements: ceiling-mounted cameras, compact edge computing units and a reliable internet connection.
Five named loss scenarios: hidden products, self-checkout non-scan, checkout skipping, manned checkout monitoring and Scan and Go losses, with mis-scans, fake scans, payment skippers and walkouts named on the self-checkout page. All of it is checkout shaped. No safety, operations or perimeter detections appear on the pages this review read, so anything outside the transaction is a scope question for the vendor.
It publishes the answer rather than leaving it to a meeting. Trigo states that it does not use biometric identification, that faces are blurred automatically, that shopper tracking is fully anonymized and that the system complies with GDPR, describing the approach as privacy by design. What does not appear on these pages is a security certification such as SOC 2 or ISO, so ask for the certification pack separately.
It depends on where the loss actually is. If it has moved past the till, or if safety and operations want answers from the same cameras, a camera-agnostic platform such as Spot AI fits, because 15+ pre-trained Video AI Agents run across the cameras already installed and deterrence runs through standard speakers rather than a notification. If the problem is the self-checkout bank and nothing else, Trigo is hard to fault. The alternatives roundup compares the field side by side.