Your floor does not wait
for a data center to answer.
Scanners, cameras, meters, and controllers already generate everything you need to catch problems early. We put a model on a server in your building and wire it to those feeds, so the decision happens in milliseconds and keeps happening when the connection drops.
The failure was visible for three shifts before anyone noticed. The reading drifted, the scanner started throwing more retries than usual, the operator mentioned it once. It was all in the logs. Nobody reads the logs, because reading the logs is nobody’s job and there are four hundred thousand lines of them.
Then the line went down for eleven hours, and afterward somebody pulled the data and found the drift, clearly, three shifts early. That is the shape of the problem in almost every facility we walk into. The signal was there. There was no system watching it.
The obvious fix is to pipe everything to a cloud analytics platform. Then you discover the two things nobody puts in the brochure: the round trip is too slow for anything happening in real time, and when the connection hiccups, your monitoring goes blind while your equipment keeps running.
What a single sensor reading has to survive
Round trip measured in hundreds of milliseconds, at best.
Decision made before the next revolution completes.
What We Wire Up
Existing Signal Capture
Controllers, scanners, meters, and cameras already on site read into one local stream. No rip and replace, no new sensor budget to justify first.
Drift & Anomaly Detection
A model that learns what normal looks like on your specific equipment and flags the slow slide before it becomes the sudden stop.
Visual Inspection
Camera feeds evaluated on-site for defects, misfeeds, missing components, and pallet or label errors, at line speed and without uploading a frame.
Event Routing & Escalation
The right person told the right thing at the right time, with escalation when nobody acknowledges. Most facilities do not have an alerting problem, they have a nobody-reads-it problem.
Offline-First Operation
Everything critical runs local. Connectivity is used for reporting and remote visibility, never as a dependency for the decision itself.
Historian & Systems Integration
Events written back into the SCADA, MES, ERP, or maintenance system you already run, so the automation shows up where your team already looks.
Hardwired Safety Boundary
Safety interlocks stay in hardware. The model advises, flags, and sequences. It does not get authority over anything that can hurt someone, and we will not build it that way.
Owned versus rented, over three years
Facilities generate events at a volume that punishes per-call pricing. Set the sliders to your own throughput and see where the lines cross.
Estimate only. Metered pricing assumes roughly 1,500 tokens per event at a blended $5 per million tokens, a mid-range figure across current commercial providers. The local column adds a 12 percent annual reserve for hardware refresh and maintenance. Your real numbers come out of the Operational Snapshot, not out of a slider.
Where latency and uptime are not negotiable
Warehouses & Distribution
Scan events, conveyor throughput, dock congestion, and mispick detection evaluated on site, at the speed the belt is actually moving.
Manufacturing Facilities
Condition monitoring, visual quality checks, and changeover sequencing on equipment that is already instrumented and already ignored.
Logistics & Yard Operations
Gate throughput, trailer utilization, and dwell time tracked from cameras and readers you already have mounted.
Utilities & Facilities
Meter drift, load anomalies, and environmental thresholds watched continuously, including through the outages when watching matters most.
The industry already conceded this argument
You do not have to take our word that production decisions belong near the production line. The telecommunications industry built a whole standards programme around it. ETSI Multi-access Edge Computing exists because a round trip to a distant data center is unacceptable for anything that has to respond inside a control loop, and the fix was to move the compute, not to optimize the network. The academic literature reached the same conclusion by a different route. The survey Machine Learning at the Network Edge catalogues four reasons practitioners keep pulling inference out of the cloud: latency, bandwidth cost, privacy, and availability. A factory floor hits all four on the same shift.
The hyperscalers concede it too, which is the part worth noticing. AWS ships IoT Greengrass and Microsoft documents offline operation for Azure IoT Edge. Two companies whose entire business is selling you cloud capacity both publish runtimes designed to keep working when the cloud is unreachable. That is not a marketing position. That is an engineering admission.
Availability is where the cost actually lands. The Uptime Institute Annual Outage Analysis tracks the causes of consequential IT outages year over year, and third-party digital service providers account for a large and growing share of them. When your inspection model lives in someone else’s region, their bad afternoon becomes your stopped line. A local system fails differently. It fails when your own equipment fails, which is a risk you can already see, staff, and insure.
On capability, the objection used to be that edge hardware could not carry a serious model. MLPerf Inference: Edge publishes measured single-stream latency for commodity edge accelerators, and the results have been settled for several benchmark rounds now. The hardware is not the constraint. The architecture is.
Finally, there is the part nobody gets to opt out of. Safety and control functions live under IEC functional safety standards and industrial network security lives under ISA/IEC 62443. Both frameworks assume a defined, defensible boundary around the control system. A vision model that reaches the public internet to decide whether a part passes is a boundary you now have to justify to an auditor. A vision model on the line is not.
How We Work
We start with an Operational Snapshot, and on a physical site that means walking it. What is instrumented, what is logged, what nobody looks at, and what the last three unplanned stoppages actually cost. Downtime has a number, and that number decides the scope.
Then one line, one cell, one dock. We prove the system catches something real before it goes anywhere near the rest of the plant. Facility-wide rollouts that begin facility-wide are how six-figure pilots quietly get switched off.
The safety boundary is fixed and not a negotiating position. Interlocks stay hardwired, and the model never gets authority over anything that can injure a person. Everything else you own outright, which is the premise of the Sovereign AI System.
References and further reading
- 01ETSI, Multi-access Edge Computing (MEC) standards programme
- 02MLCommons, MLPerf Inference: Edge benchmark results
- 03Murshed et al., Machine Learning at the Network Edge: A Survey (arXiv:1908.00080)
- 04Uptime Institute, Annual Outage Analysis 2024
- 05ISA, ISA/IEC 62443 series of standards for industrial automation and control system security
- 06IEC, Functional safety (IEC 61508 and sector standards)
- 07AWS, IoT Greengrass offline and local operation
- 08Microsoft, Azure IoT Edge offline capabilities
Sources are cited for context and verification. Vendor pricing pages change without notice, and figures quoted here reflect published rates at the time of writing. This study is general information, not legal, clinical, or engineering advice.
Find out what your last unplanned stoppage actually cost
Most operations know the hours and have never priced them properly. Request an Operational Snapshot. Thirty minutes, a full diagnostic, and the math on what the signal you already collect is worth if somebody watched it.
Request an Operational SnapshotNo sales pitch. No guilt trip. Just clarity on what's costing you.
See also: operations and scheduling systems and private document intelligence.
Built by Perry Luzier, Founder of Luzran.