UNIVERSAL EDGE INGESTION
Connects PLCs, sensors, cameras, and MES into one multi-protocol operational telemetry layer, then scales line-by-line across the plant without stopping production.
LIVE OPERATIONS STREAM
EDGE INFERENCE LOOP
SYSTEM STATE: PRODUCTION
ADAPTIVE MODEL ORCHESTRATOR
PLANT PERFORMANCE INDEX
Made for zero unplanned downtime in production lines
From operational chaosto prescriptive control
Made integrates with PLCs, sensors, and cameras to process telemetry on-site and detect anomalies in real time.
PRECISION PCB ASSEMBLY//
HIGH-SPEED BOTTLING//
INJECTION MOLDING TELEMETRY//
CNC MACHINING OPTIMIZATION//
ASEPTIC FILL & FINISH//
PRECISION PCB ASSEMBLY//
HIGH-SPEED BOTTLING//
INJECTION MOLDING TELEMETRY//
CNC MACHINING OPTIMIZATION//
ASEPTIC FILL & FINISH//
HIGH-SPEED PRODUCTION LOOP
PRECISION PCB ASSEMBLY
Solder defectsAOI pass rateCycle time
DEMO · Predictive Actions
Backed by
Backed by NVIDIA Inception to accelerate deployment of edge-native industrial AI.
Recognized by MIT as one of the 35 most innovative companies of 2025.
Backed by Startup Chile to scale high-impact industrial technology across Latin America.
How Made Works
Three layers, one operational flow.
01
DEPLOYMENT
Connects to existing factory infrastructure (PLCs, sensors, and cameras), deploys an on-site Gateway (edge computer) when minimal latency is required, or enables dedicated cloud compute when it is not.
See Deployment Layer02
ENGINE
Made OS analyzes telemetry as an interconnected operation, not isolated signals, to identify anomalies and classify events by risk and operational impact.
SEE ENGINE LAYER03
PRESCRIPTION
The Agent layer converts Engine outputs into execution-ready instructions and distributes them through operator channels for floor action.
SEE PRESCRIPTIVE LAYERDEPLOYMENT LAYER
PRODUCTION-READY INFRASTRUCTURE
Technology that synchronizes and operates industrial states from edge to cloud, with low latency and horizontal scalability.
NVIDIA EDGE INFERENCE POWER (UP TO 67 TOPS)
When minimum latency is required, Made deploys on-site hardware with up to 67 TOPS of NVIDIA inference power for real-time detection, classification, and prioritization of operational events.
RESILIENT EDGE-TO-CLOUD EXECUTION
When process tolerance allows, workloads run with multi-cloud, multi-region, and auto-scaling architecture.
Health checks, failover paths, escalation protocols, and OT/IT cybersecurity controls keep operations stable during rollout and after go-live.
ENGINE LAYER
OPERATIONAL INTELLIGENCE ENGINE
Transforms integrated plant telemetry into operational context, classified events, and prioritized risk ready for the Prescription Layer.
Factory Inputs: Cameras, Sensors, Machines
↓Gateway
↓Made OS
↓Notification System
Made OS
SMS
Customizable
Cameras
Sensors
Machines
Factory
Gateway
Made OS
Telemetry
AI Copilot
Workflows
Notification System
Made OS
SMS
Customizable
THE CORE ENGINE WHERE TELEMETRY BECOMES EXECUTION-READY PRESCRIPTION.
OPERATIONAL CONTEXT MODELING
Understands line, station, and shift behavior as one interconnected system, not isolated signals.
EVENT DETECTION & CLASSIFICATION
Identifies anomalies and true operational drift, separating normal process variation from critical events with confidence scoring.
ACTION-READY RISK PRIORITIZATION
Ranks events by potential impact and intervention window, producing a queue ready for prescriptive execution.
PRESCRIPTIVE LAYER
THE INDUSTRIAL AGENT
Turns Engine outputs into operator-ready actions: what to adjust, by how much, when to execute, and through which channel.
// IMPACT
IMPACT OF UNPLANNED DOWNTIME
25h/mo
UNPLANNED DOWNTIME
$129M
AVG. DOWNTIME COST
11%
ANNUAL REVENUE AT RISK FROM DOWNTIME
Source: Siemens, The True Cost of Downtime 2022 (published 2023)
Industry benchmarks provide context. Use the calculator below to estimate impact in your own plant.
Downtime Cost Estimator
Estimate monthly and annual downtime impact from your operating baseline.
135,000
$10k (Minimum)$260k+ (Automotive)
Active Shifts
2*Calculation based on 22 operating days per month.
30 min
1 min60 min (Critical)
Results
Estimated Monthly Loss
$2,970,000
Estimated Annual Loss
$35,640,000
Final pricing is defined after validating integration scope, line topology, and rollout plan.
Message preview to be sent
Team, I am sharing an initial downtime impact baseline for our operation: - Stop cost: $135,000 per hour - Active shifts: 2 - Average microstops: 30 min per shift - Estimated monthly downtime loss: $2,970,000 - Estimated annual downtime loss: $35,640,000 - Potential recoverable loss (20-40%): $594,000 to $1,188,000 per month / $7,128,000 to $14,256,000 per year Made can be implemented in phased windows with your OT/IT team, without stopping production lines. At your current stop-cost baseline, avoiding approximately 0.4 minutes of unplanned downtime per line per month offsets the operating investment (Month 1 threshold: 2.6 minutes per line). To schedule a demo directly, use this link: https://cal.com/madeos.ai/demo
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Frequently Asked Questions
What is Made OS for manufacturing?
Made OS is an operational intelligence system that connects plant signals, analyzes risk in real time, and delivers prescriptive actions to reduce unplanned downtime.
Does deployment require stopping production lines?
No. Deployment is staged with OT/IT teams and can roll out line by line while production remains active.
Where does Made OS run?
Made OS can run on dedicated edge compute for low-latency requirements and extend to cloud workloads where process tolerance allows.