PRESCRIPTIVE AI FOR EVERY CRITICAL ASSET.PRESCRIPTIVE AIFOR EVERYCRITICAL ASSET.

Industrial Physical AI connects software intelligence to live machines, vehicles, processes, and operator workflows. MadeOS combines PLC, sensor, camera, machine-state, vehicle telemetry, and operator data in an operational Digital Twin. It detects changing conditions, identifies the loss at risk, and prescribes a precise next action for the people responsible for operational performance.

MadeOS improves OEE by making availability, performance, and quality losses visible by asset, line, and shift. It links downtime, micro-stops, reduced speed, and process drift to their physical context, then prioritizes the action most likely to protect productive time. MadeOS goes beyond reporting OEE by helping operators act on the loss behind it.

A validated MadeOS production deployment reduced unplanned downtime by up to 55% at a furniture and wood-products factory in Mexico. MadeOS detected failure precursors, contextualized asset condition, and prescribed an intervention before loss compounded. Results vary by asset condition, available signals, production profile, maintenance response, and execution discipline.

Zero-Loss Operations is the MadeOS operating model for reducing avoidable availability, utilization, throughput, and quality loss. It applies the same discipline to factory assets and heavy-duty vehicle systems: make the physical condition visible, understand its operational consequence, and give the responsible team a precise next action before loss compounds.

Yes. MadeOS can start with a mature signal layer, partial telemetry, or an asset with no connected data. It uses existing PLC, system, and vehicle data where available, identifies critical visibility gaps, and adds only the instrumentation, machine vision, vehicle gateway, or edge connectivity required for the target loss. This creates a practical signal-readiness path without replacing the operating stack.

MadeOS can connect PLCs, sensors, historians, SCADA, MES, machine vision, process systems, REST APIs, operator input, and vehicle gateways. Common integration paths include OPC UA, MQTT, REST API, and CAN/J1939 telemetry. The final architecture is validated against equipment, network policies, data ownership, latency requirements, and OT cybersecurity controls.

Edge AI is the preferred path when an operation needs machine vision, local inference, low latency, additional signals near equipment, or local vehicle gateway processing. API access is suited to operations that already have a connected data layer and want to send operational data programmatically to MadeOS. A hybrid deployment can combine both paths when the workflow requires it.

A MadeOS deployment is designed to begin on one production-critical asset and expand in controlled stages. Signal mapping, integration, and model validation are coordinated with operations, maintenance, OT, and safety teams. A full-line shutdown is not normally required, although site access, electrical work, or instrumentation rules may require planned maintenance windows.

The right path depends on signal readiness and operating constraints. Connected factories can use existing PLC, SCADA, historian, or API data. Brownfield assets can add targeted sensors or machine vision. Heavy-duty vehicles can use a local CAN/J1939 gateway. Hybrid deployments combine these paths without replacing the controls already running the operation.

Yes. MadeOS can ingest J1939/CAN telemetry through a vehicle gateway, correlate engine load, RPM, coolant temperature, fault frames, and derate events with operating context, and return human-approved actions. Gateway selection, PGN/SPN mapping, network policy, and safety scope are validated for each vehicle platform. MadeOS does not directly control the vehicle.

MadeOS can use validated J1939/CAN signals such as engine load, RPM, coolant temperature, fault frames, derate events, duty cycle, utilization, and maintenance context. Signal availability and meaning are confirmed against each vehicle platform, gateway, and operating workflow before MadeOS uses them for a prescription.

MadeOS can identify operating conditions that may increase derate risk by connecting validated engine load, temperature, RPM, fault-frame, and duty-cycle signals with the vehicle's operating context. It does not assume a universal fault interpretation. PGN/SPN mapping, gateway behavior, service workflow, and safety scope are validated for the specific vehicle platform before use.

MadeOS validates the messages available from the gateway, including source address, scaling, and manufacturer-specific interpretation, before using them in an operating model. It correlates validated signal changes with operating context and maintenance workflow. MadeOS does not publish or infer unverified PGN, SPN, or fault-code meanings.

MadeOS uses a Kafka-based event-streaming architecture to process telemetry from multiple devices. The pipeline associates events with their source asset, supports resilient processing, and makes delivery, lag, and persistence observable as operations scale.

Kafka preserves order within a partition. By using a stable key for each device, machine, or vehicle, MadeOS can process that asset's sequence in arrival order. Source timestamps and event IDs complement the stream to identify delayed or out-of-order events.

MadeOS is designed to relate the source asset, source event, processing history, and recommended action. That traceability helps teams investigate what happened, when it happened, and what context supported an operational decision.

The MadeOS operational Digital Twin is a live model of assets, process states, events, dependencies, and operator context. It is not a static 3D visualization. The Twin reconstructs what the operation is doing now, connects an anomaly to its production impact, and gives the MadeOS prescription layer the context required to recommend the next action.

A MadeOS prescription identifies the affected asset, vehicle system, or process, the detected condition, its urgency, the expected operational impact, and the recommended next action. Depending on the configured workflow, teams can receive prescriptions through the MadeOS web application, WhatsApp, SMS, or email, with escalation and acknowledgment rules aligned to operating responsibilities.

MadeOS follows a sense, think, act, and learn workflow. It captures a validated signal, connects it to the affected asset or vehicle system and its operating context, estimates the loss at risk, and gives the responsible team a recommended action with urgency and expected impact. Team feedback and execution outcomes refine the model over time.

No. MadeOS is designed to provide human-approved prescriptions, not autonomous control. Operators, maintenance teams, and fleet teams retain decision authority. Any approved write-back to a control system requires a separately defined scope, operating safeguards, and safety validation.

No. MadeOS is an operational intelligence and prescription layer that works with existing systems of record and control. It connects relevant production, asset, and vehicle data, builds live operational context, and returns prioritized actions. The objective is to add decision intelligence without forcing a rip-and-replace program.

MadeOS starts with the operating loss, not a generic dashboard. It combines availability, performance, quality, operational context, and signal readiness with asset or vehicle-system criticality. The result is an Operational Loss Baseline that identifies where productive time is disappearing, what is driving it, and which critical system should be addressed first.

MadeOS needs the operation type, the critical asset or vehicle system to evaluate, the loss under review, available signals and systems, the operating schedule or service cycle, and the team responsible for response. This information is used to define a measurable baseline and an appropriate first action, not to produce a generic report.

Yes. MadeOS is designed for brownfield operations where modern equipment, legacy PLCs, standalone machines, vehicle systems, and manual workflows coexist. It can use available control, historian, and vehicle gateway data, add non-invasive sensing or machine vision where visibility is missing, and create a common operating context without requiring an operation-wide controls replacement.

MadeOS combines an asset baseline with production state, persistence, confidence, and criticality before prioritizing a condition. Instead of forwarding every threshold breach, it connects the detected change to expected operational loss and a recommended response. Operational feedback and execution outcomes are used to refine thresholds and escalation rules over time.

MadeOS turns machine, vehicle, and process evidence into human-approved prescriptions for operators, maintenance teams, reliability engineers, and operations leaders. Each role receives the context and next action relevant to its responsibility while the operation retains decision authority. Any write-back to an industrial control system requires a separately approved scope and safety validation.

Yes. MadeOS begins with a measurable loss on a critical asset or vehicle system, validates the signal model and operating workflow, and then reuses the proven architecture across related assets, lines, sites, and fleets. Local baselines and constraints remain specific to each operating unit, while loss definitions, governance, and performance views can be standardized across the enterprise.

The first-value path covers signal mapping, loss-baseline creation, model validation, operator workflow design, and supervised activation on a selected asset. Timing depends on brownfield connectivity, site access, integration complexity, operating variability, and validation requirements. MadeOS measures value against the approved baseline before the scope expands.