Frequently asked questions. And answers.

Ethon is the technology leader in Industrial AI. The platform is backed by 100+ scientific publications and used by many Fortune 500 manufacturers worldwide. Ethon is consistently recognized by independent institutions such as the World Economic Forum, Gartner, and Forbes for its leadership in applied AI.

Beyond recognition, Ethon is proven in large-scale, multi-factory deployments, with a forward-deployed team that helps customers move from first use cases to global rollouts with multi-million–dollar impact.

Ethon operates globally, with offices in Zurich and New York City. We serve manufacturing customers across Europe, North America, Asia, and Africa, supporting both regional deployments and large, multi-factory rollouts worldwide.

The easiest way to get started is to book a demo. Our team will understand your production setup, discuss your use cases, and walk you through how Ethon can be applied in your environment. From there, we’ll outline next steps for a proof of value or rollout tailored to your situation.

Ethon is used across discrete, continuous, batch, and hybrid manufacturing. Customers include leaders in automotive, industrial equipment, food & beverage, chemicals, pharmaceuticals, and consumer goods.
Any environment with complex processes, high variability, significant cost of scrap, downtime, or yield loss benefits from Ethon.

Customers see measurable operational impact: up to 5%+ throughput increases, up to 80% scrap reduction, and $20M+ in factory cost savings across deployments.

Beyond the numbers, teams gain faster problem resolution, more stable processes, and improvements that can be reused across lines and factories instead of being rediscovered over and over again.

Most customers connect their data and start running analyses within about two weeks. Measurable savings are typically achieved within the first three months, as deployments are supported by a forward-deployed team working side by side with customer teams to move fast and scale what works.

Ethon integrates into the reality of your factory landscape. We can implement a Unified Namespace (UNS) for you or run Ethon on top of your existing UNS and data stack, depending on what you already have in place.

Integration is delivered side-by-side with your teams by a forward-deployed team of manufacturing, IT/OT, and AI experts. We handle connectivity, data modeling, and change management so Ethon works with your processes from day one — not as a disconnected IT project.

Ethon is cloud-agnostic and runs on AWS, GCP, or Azure, supporting secure batch and real-time data ingestion.

Most analytics and ML tools show what changed by analyzing historical data or correlations. Engineers still have to interpret results, guess causes, and decide what to do next.

Ethon's platform goes much further than these point solutions. It built on the first foundation model for manufacturing, trained on billions of production scenarios to learn how processes behave. This allows Ethon to model cause–effect relationships across parameters, materials, equipment, and conditions — not just correlations.

Using agentic workflows with causal reasoning, Ethon explains why outcomes change and what to do next, helping teams stabilize processes, reduce losses, and scale improvements across production.

No. Ethon is designed for process engineers and operators to use directly in daily production.

Applications run out of the box and are driven by the platform’s reasoning capabilities and agentic workflows. Analyses are triggered automatically, results are explained in plain language, and recommended actions are directly tied to process behavior — no model building, scripting, or manual data preparation required.

Data scientists love Ethon too. They can use a Jupyter-based scripting environment to access curated production data and extend workflows.

Most deployments take around two weeks to connect data sources and start running analyses, supported by our forward-deployed engineers working side by side with your teams.

Customers typically generate measurable value within three months.

Ethon provides a unified operational view on top of heterogeneous IT/OT stacks, so factories don’t need to run identical systems to benefit from standardization.

The platform is built on a scalable, cloud-based architecture and is proven in enterprise deployments, including customers running Ethon across 20+ factories within a single organization. This allows manufacturers to standardize analytics and workflows centrally, while respecting local differences at each site.

Ethon’s Root Cause Analysis is used to investigate production issues like increased scrap rates, spec limit violations, quality issues, product recalls, process deviations, and unplanned downtime. It helps teams find the underlying causes behind drifts, instability, and performance variations across lines and factories.

Ethon connects to existing factory data sources such as MES, PLCs, Historians, sensors, and IoT platforms. The more complete the process data, the more precise the causal explanations, but even limited datasets can provide meaningful insights.

What matters most is variation, not volume. If a process always runs at one fixed setpoint, no algorithm can find what drives the problem. Ethon works with anything from 30–40 batches or parts to millions of production runs, and from about 10 to several thousand measured parameters. As long as the data shows enough change, the software can reveal true cause-and-effect relationships.

Yes. By combining automated diagnostics with causal reasoning, Ethon can identify, explain, and report issues in minutes. Customers who once spent weeks diagnosing problems that cost millions now uncover the true causes instantly and prevent them from recurring. Additionally, independent benchmarks show that our Causal Reasoning Model identifies over four times more relevant drivers of production issues than other causal discovery methods.

Process optimization focuses on continuously improving production outcomes such as yield, throughput, energy consumption, cycle time, and scrap rates. Typical use cases include stabilizing yield across shifts, reducing variability caused by raw material changes, increasing throughput without sacrificing quality, and finding parameter settings that consistently hit target KPIs under changing operating conditions.

Ethon connects to existing factory data sources such as MES, PLCs, Historians, sensors, and IoT platforms. The more complete the process data, the more precise the causal explanations, but even limited datasets can provide meaningful insights.

What matters most is variation, not volume. If a process always runs at one fixed setpoint, no algorithm can find what drives the problem. EthonAI works with anything from 30–40 batches or parts to millions of production runs, and from about 10 to several thousand measured parameters. As long as the data shows enough change, the software can reveal true cause-and-effect relationships.

Yes. By combining automated diagnostics with causal reasoning, Ethon identifies, explains, and reports optimization levers in minutes. What used to take weeks can now be done instantly—unlocking hidden potential across sites and preventing issues from recurring.

Process control usually comes after root cause analysis and process optimization. It’s about keeping lines stable that are already well-understood and instrumented. For example, adjusting control settings to counteract variability in raw material inputs.

Ethon connects to existing factory data sources such as MES, PLCs, Historians, sensors, and IoT platforms. The more complete the process data, the more precise the causal setpoint recommendations, but even limited datasets can provide meaningful ones. The closer data is streamed in real time, the faster the loop can be closed.

Process control follows root cause analysis and process optimization. EthonAI typically works with anything from 30–40 batches or parts to millions of production runs, and from around 10 to several thousand measured parameters.

Most deployments take about two weeks to connect data sources and start running analyses. Customers typically generate value within three months.

Early fault detection is used for predictive maintenance, continuous condition monitoring, and anomaly detection to prevent downtime and quality losses. Typical use cases include detecting mechanical wear before failures occur, identifying abnormal vibration or temperature patterns, spotting process deviations that lead to batch losses, and catching early signs of equipment or process degradation before they impact yield or throughput.

Ethon connects to existing factory data sources such as MES, PLCs, Historians, sensors, and IoT platforms. The better the sensor deployment, for example existing vibration sensors on machines, the more accurately issues like machine downtime can be predicted. And the closer the data is streamed to real time, the faster alerts can be flagged.

The longer the period of normal operating behavior, the better the model can learn what “normal” looks like. In most cases, it’s enough if the data captures the usual day-to-day variation in the process.

Most deployments take about two weeks to connect data sources and start running analyses. Customers typically generate value within three months.

Visual quality inspection focuses on catching defects that existing sensors can’t detect. It’s about meeting quality standards expected by customers. For example, detecting broken biscuits, surface scratches, assembly errors, or molding misalignments before products leave the line.

Ethon requires a golden sample of 25–50 defect-free images to set up a job. In addition, customers should plan for line integration. Ethon integrates flexibly with automation systems via REST API, but it’s important to define what actions should be triggered based on OK/NOK results or returned metadata.

Ethon requires a golden sample of 25–50 defect-free images to set up a job. The more variation included in what’s considered “good”—including edge cases—the better the model can learn to distinguish acceptable from defective parts.

End-to-end implementation, including setup consultation, on-site training, and integration with common line automation, usually takes about two weeks.

Production traceability shows where each batch or part has been, what happened to it, and under which conditions it was processed. It can be used to track batches in process industries or trace individual parts through complex assembly lines. By unifying events from machines and systems into a single view, it makes material flow transparent across the line and reveals bottlenecks, rework loops, and deviations from the intended process.

Ethon connects to existing factory data sources such as MES, PLCs, Historians, sensors, and IoT platforms. The more complete the process data, the more accurately material can be tracked. For example, timestamps and batch IDs help reconstruct the exact routing through production.

There’s no lower limit. As soon as production becomes complex enough that material flow can’t be easily understood at a glance, traceability starts to add value and reveal optimization opportunities.

Most deployments take about two weeks to connect data sources and start running analyses. Customers typically generate value within three months.