What RetailNext's own documentation says its retail analytics platform does not cover, written for loss prevention and store operations teams running due diligence. Every constraint carries its documentation, its operational impact, and its workaround.
Every constraint below comes from RetailNext's own current documentation, checked on August 17, 2026. Where RetailNext documents nothing, this page says so rather than guessing, because an absence in a document is a question to ask rather than a finding to claim. Spot AI sells a competing platform, so nothing here rests on an anonymous source or an aggregated user rating, and this page corrects something Spot AI has itself published about RetailNext. We wrote that no camera compatibility statement appears: the Asset Protection module states compatibility with existing analog and IP camera infrastructure with no forced hardware replacement, and retention is published too, at up to 30 days. The protocol behind that statement genuinely is unpublished, and constraint 05 below says so.
Two halves feed the platform: a sensor RetailNext builds, and the cameras a store already owns. The commercial side is published in unusual detail. Three things past the case file are not published at all.
A constraint list is only useful next to an honest account of the product.
A three-step configuration returns a cost per store per month and per year in under 60 seconds with no commitment required, and the inclusions are listed next to it: Aurora hardware sensors, full platform access on desktop and mobile, all software licensing and updates, an unlimited Aurora sensor warranty and access to Benchmarks market data. The four variables that move the number are named too. Almost nothing else in this category makes that possible.
RetailNext states compatibility with existing analog and IP camera infrastructure with no forced hardware replacement, and the module blends video with point-of-sale exceptions, EAS alarms, door alarms, safe openings, keypad activity and panic buttons into searchable case files, with investigation stated as up to 75% faster. For a chain with 200 stores and a decade of mixed hardware, the half a loss prevention team uses daily does not start with a camera purchase.
Apparel and footwear, jewelry, department stores, convenience stores, restaurants, retail banking and shopping malls are named individually, and Benchmarks market data lets a chain compare its own numbers against the market rather than only against last year. Named technology partners including Zipline, Rallyware, Storeforce Solutions and Zebra connect that data to workforce and CRM systems.
Each one is a consequence of how the platform is designed rather than a defect. What matters is whether it collides with your starting point.
RetailNext publishes the four variables that move the number: store count, entrances per store, store type and global region. The subscription covers Aurora hardware sensors, platform access, all software licensing and updates and the sensor warranty together. It also states that the estimate covers subscription software and hardware costs only, with additional services varying based on specific site requirements.
A 150-store chain with two entrances per store is priced on 300 entrances and the Aurora sensors behind them, whichever half of the platform the team actually opens every day. If the daily users are the loss prevention analysts working case files off cameras already on the wall, the curve is still set by the traffic-counting side. Installation and any custom integration sit outside the figure the estimator returns.
Run the estimator at your real store count and entrance count rather than an average, then ask for the services quote separately so the two numbers stay distinguishable. Ask directly what the subscription looks like for stores where only asset protection is deployed, and confirm whether the entrance count or the store count drives the renewal.
RetailNext publishes up to 30 days of high-resolution color video held in the RetailNext cloud. That is a stated figure rather than a vague one, and it is the published ceiling for how far back a case can reach inside the platform without an export.
Organized retail crime cases, insurance claims and civil recovery routinely run past 30 days, and a repeat-offender pattern is often only visible across a quarter. A district manager assembling a case in week six is working from whatever somebody thought to download in week three, so evidence handling becomes a discipline rather than a search.
Check the window against how long your own cases actually stay open, then write an export rule into the loss prevention procedure so anything with a case number leaves the platform inside the first fortnight. Ask RetailNext in writing whether a longer retention option exists for the stores that need it and what it costs, and keep a local recorder for the sites with the longest evidence obligations.
RetailNext publishes real-time alerts and searchable case files blending video with point-of-sale exceptions, EAS alarms, door alarms, safe openings, keypad activity and panic buttons, with investigation stated as up to 75% faster. No on-site audio, strobe or horn behavior appears anywhere. The documentation covers what an event becomes inside the platform, not what happens on the shop floor at the moment it fires.
During trading hours this is the correct design, because a case file is what actually recovers money and a store colleague should not be sent to confront anybody. Outside trading hours it is the whole gap: a back-door alarm at 02:00 produces a well-built record of an event nobody interrupted, and the deterrent value stays with whatever alarm system the chain already pays for.
Split the requirement by hour. Keep RetailNext for the trading-hours case work it is built for, and for closed hours name the product that drives the speakers and strobes already in the store, then price it in the same quote rather than in a later phase. Where the alarm contract is up for renewal, that is the moment to combine the two conversations.
RetailNext publishes traffic counting, shopper behavior and merchandising insight on the analytics side and asset protection on the other, with Benchmarks market data alongside. No safety or operations detection set appears: spills, blocked fire exits, ladder use, stockroom lifting or back-of-house procedure adherence are not named anywhere in the published capability set.
The same ceiling cameras are usually being asked three different questions by three different budget holders. RetailNext answers the merchandising question extremely well and the shrink question well, and the health and safety question not at all. A convenience chain whose insurer is asking about slips and falls ends up with a second platform reading the same feeds.
List the questions your cameras are expected to answer and mark which function is asking each one. If more than half sit outside retail analytics, price a platform that covers security, safety and operations on the same cameras alongside RetailNext rather than after it, and decide deliberately whether one estate can carry two vendors.
An AICPA SOC seal appears on the platform pages and the report type is not publicly specified. No camera protocol, conformance profile, resolution floor or frame-rate range appears for the asset protection module either, beyond the statement that it is compatible with existing analog and IP camera infrastructure. Both are absences of published detail rather than evidence of a weak answer.
A retail IT team cannot tell from public pages whether the fifteen-year-old analog run at the back of the older stores qualifies for the module, and a security reviewer cannot tell which SOC report is being claimed. Both questions arrive late in a procurement cycle, and both are cheap to answer early.
Request the SOC report with its type and date, and ask which protocol the asset protection module reads and at what minimum resolution and frame rate. Then run a two-store test on your oldest cameras, not your newest, before the estate rollout is scheduled: the older stores are the ones that decide whether the module covers the whole chain.
The same five constraints in one view, sized to paste into an evaluation document.
Swipe the table sideways to see every column.
RetailNext data comes from RetailNext's own product, pricing, asset protection and partner documentation, checked on August 17, 2026. Gaps are marked as not publicly specified.
If the first question the business asks about its stores is what shoppers do inside them, most of the constraints above stop applying. A chain in apparel, jewelry, department stores or malls, with merchandising and store operations funding the project and loss prevention working cases at the register, is buying exactly what this platform was built for. Aurora measures the entrance to a standard a general video platform is not designed to hit, Benchmarks puts your numbers next to the market, and the published estimate makes the budget conversation short.
Spot AI approaches the same cameras from the other end, which is why its constraint list looks different. Rather than a retail analytics suite with an asset protection module beside it, the platform ships 15+ pre-trained Video AI Agents that run across the whole estate: cash register theft, after-hours intrusion, vehicle break-in, fire, personal protective equipment, forklift near-miss, falls and crowding in hazard zones. Iris builds anything not on that list in natural conversation in about eight minutes, so a stockroom question and a forecourt question do not become two more procurements.
The architecture answers the retention and response constraints directly. Full-resolution video stays on the Intelligent Video Recorder in the store and only event metadata crosses the network, so how far back a case can reach is a function of the recorder in the building rather than a plan tier. Any ONVIF or RTSP IP camera works at full functionality and legacy analog comes in through the IVR, and an event at 02:00 is answered on site with talk down, strobes and horns through standard speakers rather than by a record for the morning.
The useful question is not which platform has fewer constraints, but whether your cameras are being asked a merchandising question or an incident question.
None of that makes Spot AI the right answer for every retailer, and on a merchandising shortlist it is not the same product. RetailNext publishes a cost per store per month that a buyer can act on in 60 seconds. Aurora is a purpose-built entrance sensor with the analysis running onboard, and Spot AI has no equivalent dedicated counting device, so entrance accuracy depends on the cameras in the ceiling. Benchmarks market data has no counterpart either, and merchandising, product placement and omnichannel fulfillment analysis is outside what a video AI platform sets out to do. A constraint list earns its keep by lining each platform's shape up against the question your stores are actually asking.
A live pilot on your store cameras answers in a week what a spec sheet cannot.
Customer-reported outcomes from named Spot AI customers.
All Star Elite cut cash shrink from about 6% to 1% across its stores and reported investigations more than 50% faster.
Don Franklin Family of Dealerships had incident footage on responding officers' phones within four minutes of an alarm, and reported five recoveries within the hour.
Blackmon Oil Co. runs a vehicle loitering filter across its round-the-clock stores so the lot stays watched with a smaller overnight crew.
"Spot AI is easy to use, IT is happy it's web-based and connects to our cameras inside, and our employees feel safer in their parking lots."
Five, all documented. The subscription is a bundle priced on store count, entrances per store, store type and region, with services outside the published estimate. Retention is published at up to 30 days of high-resolution color video. The documented response is a real-time alert and a searchable case file, with no on-site audio, strobe or horn behavior published. The capability set covers shopper behavior, merchandising and asset protection, with no safety or operations detections. And the SOC report type and the module's camera requirements are not publicly specified.
Yes, and the correction is worth making plainly. The Asset Protection module states compatibility with existing analog and IP camera infrastructure with no forced hardware replacement, so a store does not have to be recabled to run it. The analytics half is different by design: Aurora is a ceiling-mounted PoE sensor RetailNext designs and manufactures, with the analysis running onboard and stated not to depend on the store's existing cameras. What is not publicly specified is the protocol or resolution the module needs.
Up to 30 days of high-resolution color video in the RetailNext cloud, which RetailNext publishes as its retention figure. That covers most shrink investigations and falls short of what organized retail crime, insurance and civil recovery cases often need. The practical answer is an export rule: anything that acquires a case number should leave the platform well inside the window, and a longer retention option is a question to put in writing.
Not in the published capability set. Traffic counting, shopper behavior, merchandising insight, Benchmarks market data and asset protection are what the pages describe, and no safety or operations detection appears: spills, blocked fire exits, ladder use or back-of-house procedure adherence are outside scope. If the insurer or the health and safety function is part of the business case, that split is worth settling before a shortlist closes rather than after a rollout.
It depends which question the cameras are being bought to answer. For traffic, dwell and merchandising, RetailNext is built for it and prices itself in public. For a chain that needs the same cameras to cover register theft, after-hours intrusion, safety on the floor and operations in the back of house, a camera-agnostic platform such as Spot AI fits: 15+ pre-trained Video AI Agents run on any ONVIF or RTSP camera plus legacy analog through the Intelligent Video Recorder, footage stays in the store, and an incident is answered with talk down, strobes and horns.