A documented comparison for retail teams weighing a store analytics platform against a video AI one. RetailNext's centre of gravity is shopper behaviour and merchandising, with asset protection as one module. Whether that is the right centre depends on which department is signing.
RetailNext is an AI retail analytics platform built around what it calls the richest in-store dataset in retail, aimed at merchandising, product placement and omnichannel fulfilment, with an asset protection line that identifies anomalous shopper behaviour patterns at scale and builds case files from video-linked event data. Teams shop for an alternative when shrink rather than merchandising is the budget holder, when the same cameras also have to cover the back room and the lot, or when the platform has to act rather than analyse. The five below split on that.
Both platforms read cameras inside a store. They differ on which question the data is collected to answer, and that usually maps to which budget is paying.
Every cell is something the vendor publishes, or an explicit not publicly specified.
Swipe the table sideways to see every column.
RetailNext's row was read off retailnext.net on August 12, 2026, including the asset protection wording and the merchandising framing. Its architecture and camera compatibility are absences on those pages rather than gaps in this research, and they are the first questions to put to the vendor. The other five rows come from each vendor's public documentation reviewed in July and August 2026 on the same five columns.
Start by placing it correctly, because the honest comparison depends on it. RetailNext is a retail analytics platform first: its published promise is the richest in-store dataset in retail, used for merchandising, product placement and omnichannel fulfilment, with asset protection as a module that identifies anomalous shopper behaviour at scale and links video to event data for case files. If the person signing is a merchandising or customer experience leader, that is the right centre of gravity and most of this page is aimed elsewhere.
The first real reason teams look further is that shrink programmes are usually funded and run separately. A loss prevention director wants the register exception, the concealment sequence, the case file and the evidence trail, and wants them tied to transaction data. Solink names Toast, Square and NCR for exactly that. RetailNext's asset protection sits inside a platform whose other tenants are merchandising and traffic, so the question to ask is whose roadmap the shrink features are on.
The second is everything outside the sales floor. Store cameras also cover the back room, the loading door, the staff entrance and the lot, and those views raise safety and security questions rather than shopper ones. RetailNext publishes no camera compatibility statement, no deployment model and no on-site action, so a chain that needs the whole estate answered is looking at more than one contract on cameras it has already paid for.
Merchandising analytics and shrink prevention are usually two budgets with two owners. Decide which one is buying before comparing platforms that lead with different halves.
What each one is, where it is strong, and what to check before you commit.
Spot AI is a software-led video AI platform that turns the IP cameras a business already owns into AI coworkers. An on-site Intelligent Video Recorder keeps full-resolution video in the building and sends only event metadata to the cloud, so search and multi-site management stay cloud-based while footage does not leave the facility.
Solink is a cloud video platform for retail and restaurant operators that pairs a site's existing cameras with point-of-sale data, so register exceptions surface as video clips. It ships three named agents, Overnight Guard, Loss Prevention and Store Readiness, alongside a customer-facing builder, and names 48 camera manufacturers publicly.
Verkada is a cloud-managed platform that sells its own cameras, access control, sensors, alarms and intercoms under one console, and publishes per-device MSRPs, which is unusual in this market. Third-party cameras come in through Command Connector, which Verkada states is an ONVIF Profile S conformant client and can also ingest RTSP feeds.
Coram AI is a camera-agnostic platform built around AI search and investigation across cameras a business already owns, with named detections for firearms, falls and PPE violations, and alerts that can be built in plain English. An on-site appliance called Coram Point is purchased upfront.
Eagle Eye Networks is a cloud video management system that connects to virtually any ONVIF-conformant camera and digitises analog feeds through analog-ready Bridges and CMVRs, then layers cloud analytics on top of the estate. Its documentation names ONVIF Profile S and dual codec streaming as the integration path.
Six questions that separate these platforms faster than any feature list.
This settles the shortlist faster than any feature. Merchandising and customer experience buy shopper analytics. Loss prevention buys case files, register exceptions and evidence. Operations buys queue length and opening checks. Ask which of those the platform leads with, and whether the others are modules or roadmap.
Video alone builds half a case. Solink names Toast, Square and NCR for point of sale exception reporting. RetailNext links video to event data for case files without naming point of sale integrations on the pages read. Ask each vendor which register systems they name, because a platform that integrates with yours removes an investigation workflow you would otherwise run by hand.
RetailNext publishes no camera compatibility statement, so this comes first. Solink names 48 camera manufacturers. Eagle Eye publishes more than 7,500 models plus analog through Bridges. Spot AI takes any ONVIF or RTSP camera and legacy analog through the Intelligent Video Recorder. Run every answer against your oldest store, not your flagship.
Count the views: sales floor, back room, loading door, staff entrance, lot. Shopper analytics is aimed at the first one. Ask each vendor which of the rest they cover and with what detections, then compare the number of contracts each answer implies across the whole chain.
Establish what the store gets after closing, when there is nobody left to call. Eagle Eye documents sirens and talk-down alerts. Verkada documents deterrence tied to its BZ11 horn speaker or Intercom models. Spot AI runs talk down, strobes and horns through standard speakers already in the store. RetailNext and Coram publish no deterrence behaviour.
Nobody on this page publishes a price except Verkada, which publishes per-device MSRPs. Put the same store list in front of every vendor, itemised into software, hardware, installation and storage, and ask for it at one store and at your full count. Per-store economics move the answer more than any feature does.
Spot AI fits when the cameras are already in the stores and the estate needs shrink, safety and operations answered from the same feeds, with something happening after the alert. It runs on any ONVIF or RTSP camera plus legacy analog through the Intelligent Video Recorder, ships 15+ pre-trained agents plus Iris for custom detections, and deters through standard speakers already installed.
RetailNext stays the right call for a retailer whose primary question is what shoppers do and how product placement performs, with asset protection wanted alongside rather than instead. On in-store shopper data its published position is the strongest here, and a comparison that treated it as a security platform would be comparing the wrong halves.
The cameras are already paid for and already pointed at the right places. The question is how many of your departments one contract is allowed to serve.
Watch the AI coworkers work on your live feeds, not a demo reel.
Customer-reported outcomes from named Spot AI customers.
All Star Elite brought cash shrink down from about 6% to 1% across its store estate, and cut investigation time by more than half.
All Star Elite also lifted sales 5 to 15% by using the same footage to optimise where best-selling stock was placed.
Don Franklin Family of Dealerships lifted camera utilization 60% as the system became an analytical tool across departments rather than a security archive.
"The ability to formalize our incident reporting with Spot AI, keep every case in one database, and attach video directly to those cases has been a game changer."
The five covered here are Spot AI, Solink, Verkada, Coram AI and Eagle Eye Networks. Spot AI fits chains needing shrink, safety and operations from the same cameras. Solink fits register-anchored loss with point of sale exceptions. Verkada fits a chain specifying hardware from scratch. Coram AI fits investigation-first teams. Eagle Eye Networks fits mixed and partly analog estates.
Not primarily. Its published centre is retail analytics, built on what it calls the richest in-store dataset in retail, for merchandising, product placement and omnichannel fulfilment. It does publish an asset protection line that identifies anomalous shopper behaviour at scale and builds case files from video-linked event data, so shrink is covered as a module rather than as the product.
Its pages do not say. No protocol, conformance profile or compatibility list appears publicly, which for a platform reading in-store video is the first thing to establish. Ask for the stream requirements in writing and check them against the oldest store in the estate, because that store decides the project.
They approach it from opposite ends. RetailNext looks for anomalous shopper behaviour patterns at scale inside a broader analytics platform. Solink anchors on the register, naming Toast, Square and NCR, so an exception surfaces with the transaction attached. If your losses show up at the till, the transaction link is the requirement rather than a preference.
No. No price, plan tier or contract term appears on its site, which is the norm across this category. Of the platforms compared here only Verkada publishes per-device MSRPs. Any comparison has to come from quotes on the same store list, at one store and at the full count, with hardware itemised separately.