Invisible AI is a station-level instrument. It ships its own edge devices with depth-sensing cameras, keeps everything inside the factory firewalls and measures what happens at a workstation. This is the one page in the set where camera reuse is not common ground, so the plan is about what the cameras already in your ceiling could be doing instead.
One platform instruments a workstation with its own device, the other reads the cameras already covering the building. Both rows describe what each vendor publishes.
Four moments when manufacturing teams running a station-level tool start this conversation. If none of them are true, staying put is a reasonable answer.
The instrument is the device. Invisible AI describes edge units containing an Intel RealSense 3D camera, an NVIDIA chipset and up to 4TB of SSD, with storage stated per device. That is the right shape for a station you are studying closely. It is an expensive shape for a question that spans a building, because every new area is another unit rather than another camera already on the wall.
Most plants running a station tool already have IP cameras over the aisles, the dock and the yard, feeding a recorder nobody opens unless something goes wrong. Those feeds are the cheapest data in the building because they are already paid for. If nothing is watching them, that is the gap worth pricing before another station device is ordered.
Invisible AI publishes production and process intelligence: cycle time variability, process drift, throughput, ergonomic risk and quality deviations at the station, with alerts to industrial engineers and team leads. Safety, security and yard or perimeter coverage are not publicly specified. When EHS and security start asking for video, they are asking a different vendor by default.
A rebalanced line or a new model year moves the stations, and a device-per-station estate has to follow. Worth asking both vendors what a layout change costs in practice: for one it is moving hardware, for the other it is pointing an agent at a different camera. Neither answer is automatically better, and the honest version depends on how often your lines actually move.
The structural differences that decide whether a switch is worth starting.
Swipe the table sideways to see every column.
Invisible AI data comes from Invisible AI's own public product, how it works and security pages, read August 2026. Where Invisible AI publishes nothing, this table says so rather than guessing.
A rollout that keeps coverage live throughout. Most sites go live inside six weeks.
Build two inventories side by side, because they barely overlap. First the edge devices: which stations they sit on, what each one measures and which of those measures the continuous improvement program actually uses in a review. Then the cameras already in the plant that no software reads: the aisles, the dock doors, the yard and the perimeter. The second list is usually longer than anyone expects, and it is the whole argument for the move.
This pilot is additive rather than a swap, which makes it unusually cheap to run. Leave the edge devices exactly where they are and put Spot AI on the overhead cameras nobody is reading. Two weeks later you have a real answer to the only question that matters: how much of what you needed the station devices for is visible from the ceiling, and how much of the plant was invisible until now.
Decide area by area rather than in one call, and be honest about the exception. Where the measurement genuinely needs depth at the workstation, that station keeps its device and nothing in this plan asks otherwise. Everywhere else, the overhead cameras take over and the device comes off. Export what you need first: Invisible AI states storage of up to 3 months and 2TB per device on the edge, so removing a unit is the moment its history stops being reachable.
Once the line is covered, the work moves to everything the station instrument was never pointed at. Add SOP adherence and shift recaps on the same feeds, put safety agents on the aisles and the dock, bring the yard and the perimeter into scope, then build an Iris detection for something specific to your process. This is the phase where the plant stops having one instrumented line and starts having an instrumented building.
The six that come up in every one of these conversations.
That matters less here than on most switches, because the pilot is additive: the edge devices keep running while Spot AI reads cameras they never touched. Run the overlap during the contract, use it to decide area by area and time any device removal to the renewal date rather than to the pilot.
No, because nothing is switched off to run the comparison. The devices keep measuring their stations throughout the pilot, and a station only gives up its device once the area has been signed off. If a station turns out to need depth at the workstation, it keeps the device and the plan moves on around it.
They are different instruments and the honest answer is that it depends on the measure. Invisible AI publishes synchronized depth-sensing cameras at the station. Spot AI reads the standard IP cameras already overhead, with operations agents covering SOP adherence, scorecards, dwell and shift recaps. Test both on your own line in the pilot against the measures your CI reviews actually use, and let that decide rather than a datasheet.
Invisible AI states all video processing and storage, up to 3 months and 2TB per device, happens on the edge without leaving the factory firewalls. There is no cloud archive to negotiate over, which is the good news and the catch: the day a device comes off a station is the day its history stops being reachable. Export what still matters before that, per device.
That cost is real and worth naming. Map the reports first, in the assess phase, and treat rebuilding them as a deliverable of the pilot rather than something to do afterwards. The offset is that the same reviews start covering areas that were never instrumented, because the cameras feeding them were already installed.
It depends on how many areas you want covered, and anyone answering without that number is guessing. One model scales by devices bought per station, the other by cameras already on the wall. Quote both against the identical coverage list, include installation on each side and be explicit about which stations keep their device in either scenario.
Bring a camera list and we will tell you what works as it is.
Customer-reported outcomes from named Spot AI customers.
Staccato went from first conversation to full deployment across an 800-acre campus in seven weeks, on the camera infrastructure it already had.
Bridge33 Capital standardized video across 25 plus properties, each acquisition arriving with a different camera system, and cut footage search from hours to minutes.
The YMCA of Greater Richmond deployed 17 locations in two weeks and standardized retention across the estate.
"Within four minutes of the alarm going off, Spot AI gave us video footage of the incident on the responding officers' phones. We had five recoveries within the hour."
Not as a condition of anything. The devices sit on the stations they instrument, and Spot AI runs on the standard IP cameras across the rest of the plant, so the two do not compete for the same hardware. Most plants decide area by area during the pilot, and stations that genuinely need depth at the workstation keep their device.
The ones already in your ceiling. Spot AI is camera agnostic: any IP camera speaking ONVIF or RTSP works as it is, whatever the manufacturer, and legacy analog runs come in through the Intelligent Video Recorder. In most plants running a station tool, those cameras exist already and no software is reading them.
Most plants are live inside six weeks: a week to build the two inventories, a fortnight of additive pilot on one site, then an area-by-area decision. Because nothing is switched off during the pilot, the timeline is driven by how quickly the plant can agree the area list rather than by any technical cutover.
Not on either platform. Invisible AI states all processing and storage happens on the edge without leaving the factory firewalls. Spot AI keeps full-resolution video on the Intelligent Video Recorder in the building and sends only event metadata across the network, which is what lets search and multi-site management run in the cloud.
Instrument versus estate. Invisible AI puts a purpose-built depth device on a workstation and measures the cycle in detail. Spot AI puts 15+ pre-trained agents across security, safety and operations on the cameras already covering the whole building, with AI Talk Down, strobes and horns through standard speakers where a response is needed.