A documented comparison for teams drawn to a platform where you define the objects and actions yourself. That is a real capability and worth shortlisting for. What Vidan AI does not publish is what ships pre-trained, which cameras qualify, or where the software runs.
Vidan AI sells real-time video monitoring built around customizable object and action detection, letting a team define what to watch for, with named examples including running in restricted areas, opening secure doors and prolonged inactivity in high-risk zones, across city, industrial, hospitality and traffic use cases. Teams shop for an alternative when they want a pre-trained set as well as a custom one, when camera compatibility and deployment have to be documented, or when something has to happen on site after the alert. The five below answer those differently.
Both platforms end with detections running on the cameras a site already has. They differ on how many of those detections you have to define yourself before anything is watching.
Every cell is something the vendor publishes, or an explicit not publicly specified.
Swipe the table sideways to see every column.
Vidan AI's row was read off vidan.ai on August 12, 2026. The detection examples are its own published ones; the blank cells are absences on its public 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 giving Vidan the point it deserves, because it is a real one and it is unusual to lead with. Most platforms in this category sell a fixed catalog and treat anything outside it as a roadmap conversation. Vidan sells the opposite: complete control over what matters most, by letting you define the specific objects and actions to monitor. For a site whose risk is genuinely peculiar, a platform built around that premise is a better starting point than one built around a list you have to fit into.
The first real reason to look further is that custom detection is a floor, not a ceiling. The published examples, running in restricted areas, opening secure doors, prolonged inactivity in high-risk zones, are specific and useful, but there is no pre-trained catalog published alongside them. Every requirement you have becomes something to define, configure and validate. Ask how long one detection takes to build, who builds it, and what happens when it misfires, then compare that with a platform where the common cases arrive already trained and only the odd one needs building.
The second is everything the pages leave unstated: no camera compatibility, no protocol, no deployment model, no pricing, no certifications, and no on-site action after an alert. For a software platform whose whole premise is running on the cameras a site already has, camera compatibility is question one, and it is currently a phone call rather than a document. None of that means the product is weak. It does mean the evaluation happens entirely in a pilot, so agree in writing what a successful month looks like before it starts.
Define-it-yourself is the right architecture for an unusual requirement and the wrong one for an ordinary list. Count how many of your detections are actually unusual.
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.
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.
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.
Rhombus sells cloud-managed cameras, sensors, access control and alarm monitoring as one system, described by the company as a cloud-edge system that operates offline. Third-party cameras come in through the Relay Connector N100, and the company publishes SOC 2, NDAA, GDPR and TAA compliance badges.
Six questions that separate these platforms faster than any feature list.
Write the list, then mark each line common or peculiar. PPE, forklift near-miss, tailgating, loitering, intrusion and queue length are common, and a platform that ships them pre-trained skips the build. If most of your list is peculiar to your process, a platform built around defining detections yourself starts ahead. Spot AI covers both, shipping 15+ agents plus Iris for the odd one.
Ask for the mechanism and the elapsed time, not the possibility. Spot AI states a custom detection in about 8 minutes with Iris, without a data science team. Vidan publishes the capability without a stated build time or owner. Coram lets alerts be built in plain English. Get each answer in writing, then ask to build one live during the trial.
Vidan publishes no camera compatibility statement, so this comes first. 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. Coram says any IP camera without naming a protocol. Test every answer against your oldest camera.
No deployment model appears on Vidan's public pages. Eagle Eye stores video on its own data centers. Spot AI keeps full-resolution video in the building and sends only metadata. Rhombus describes a cloud-edge system that operates offline. On a segmented network this decides whether a pilot is even permitted, so ask before you demo.
Work out what a detection does at 02:00 once it has fired and nobody is watching. Eagle Eye documents sirens and talk-down alerts. Verkada gates automated audio to the BZ11 or an Intercom. Spot AI runs talk down, strobes and horns through standard speakers already mounted. Vidan, Coram and Rhombus publish no deterrence behaviour, so the escalation path ends with a person.
Vidan publishes no accuracy figure, no integration list, no certifications and no pricing. Neither do several larger vendors here. So the only real evaluation is a pilot on your own cameras: agree which detections, over how many weeks, measured against what baseline, and what counts as a miss, all in writing before it starts.
Spot AI fits when the common detections should arrive pre-trained and the peculiar one should still be buildable. It runs on any ONVIF or RTSP camera plus legacy analog through the Intelligent Video Recorder, keeps full-resolution video in the building, ships 15+ agents across security, safety and operations, adds Iris for a custom detection in about 8 minutes, and acts through standard speakers on site.
Vidan AI stays a reasonable call for a team whose requirement is specific enough that defining it beats choosing from a catalog, that is happy to settle cameras, deployment and cost in conversation, and that has the time to run a proper pilot. The customisation premise is a genuine differentiator and the blank columns are the work.
A camera-agnostic platform lets you test the AI on your own cameras before any hardware decision, which is the cheapest way to de-risk the choice.
Watch the AI coworkers work on your live feeds, not a demo reel.
Customer-reported outcomes from named Spot AI customers.
Silver Bay Seafoods lifted operational efficiency 15% and improved PPE compliance across 22 locations after replacing fragmented legacy camera systems.
Don Franklin Family of Dealerships lifted camera utilization 60% as the system became an analytical tool across departments rather than a security archive.
Cambridge City cut the time it takes to find footage from two hours to thirty seconds.
"With Spot AI, we're focused on three things: safety, productivity, and security."
The five covered here are Spot AI, Coram AI, Eagle Eye Networks, Verkada and Rhombus. Spot AI fits teams wanting a pre-trained agent set plus a custom-detection path with deterrence on top. Coram AI fits investigation-first teams. Eagle Eye Networks fits mixed and partly analog estates. Verkada fits a site specified from scratch. Rhombus fits mid-market teams wanting one vendor.
Its pages do not say. No protocol, conformance profile or compatibility list appears publicly, which for a software platform is the first thing to establish. Ask for the stream requirements in writing and check them against your oldest cameras, because that is the camera that decides whether this is a software project or a hardware one.
Its premise is customizable object and action detection, where you define what to monitor. Published examples include running in restricted areas, opening secure doors and prolonged inactivity in high-risk zones, plus crowd management, vehicle anomaly detection and hospitality use cases. A pre-trained catalog and any deterrence behaviour are not publicly specified.
It depends how ordinary your list is, and the honest answer is that most lists are mostly ordinary. Pre-trained wins on the common cases because integration and validation work is the expensive part. Custom wins on the case nobody ships. A platform offering both, such as Spot AI with 15+ agents plus Iris, avoids choosing between them.
No. No price, plan tier or contract term appears on its site, and no accuracy figure or certification either. That is not unusual in this category, but it does mean the whole evaluation moves into the pilot. Insist on the same site list from every vendor and agree the pilot's success measure in advance.