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The rapid advancement of AI across its core disciplines, Generative AI, Machine Learning, Edge Computing, Computer Vision, and Deep Learning, has created an entirely new set of opportunities to act on that data and connectivity.
Most connected machine manufacturers are not capitalising on the AI opportunities their data creates. The barrier is rarely ambition; it is the gap between a promising pilot and a capability that scales.
Gain structured visibility into how machines behave, are used, and perform once they leave the factory.
Prevent failures and quality defects before they occur, rather than responding after the fact.
Transform isolated AI projects into a governed, fleet-wide capability that compounds with every machine.
Apply connected machine data to turn aftermarket, field service, and reporting into competitive strengths.
Once equipment leaves the factory, what happens to it is largely invisible. Structured visibility into real-world machine behaviour changes that across product, service, and commercial teams
Turn billions of raw sensor events into a structured, queryable asset every team can act on.
Transform raw telemetry into labelled usage patterns without requiring pre-labelled training data.
Flag deviations from normal operating behaviour early and pinpoint which parameters are responsible.
Connected equipment creates the conditions for a structurally different relationship between manufacturer and customer, one founded on guaranteed performance rather than reactive maintenance.
Make predictive maintenance viable from day one, even without historical failure data.
Inspect every unit at full production speed with accuracy no human inspector can sustain across a shift.
Individual AI initiatives deliver value in isolation. Extending that value across a global fleet, multiple teams, and a broad portfolio of applications is where complexity becomes the primary obstacle.
Give distributed teams a governed MLOps platform to develop, validate, and deploy models without losing quality or control.
Improve models across the entire fleet without centralising a single customer's data.
The service operation is the function that most directly determines customer retention and lifetime value. Connected machine data makes it possible to serve customers far more effectively, but only where the service organisation has the infrastructure to act on it.
Turn a reactive aftermarket operation into a proactive revenue engine with AI-powered forecasting and replenishment.
Make your best engineers' knowledge available to every field technician from day one.
Replace manual reporting with a continuous, auditable output built directly into your data architecture.
Build the verified performance record that makes outcome-based contracts commercially credible and defensible.
Our step-by-step process helps you identify where you are, what is blocking progress, and what to build next to move from data foundation to commercial platform.
We start with what you are actually trying to achieve in business terms: reduce unplanned downtime, improve service margins, understand how your machines are really used in the field, or create the basis for a new service model.
We translate business objectives into actionable technical use cases and AI opportunities grounded in what is feasible for your machine, data, and service environment.
We assess your current position across data infrastructure, modelling capability, deployment readiness, and organisational maturity.
We design a phased execution plan that balances short-term value with long-term capability building across the installed base.
We move into delivery through an embedded AI product team or by strengthening your internal team with Faktion specialists.
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Trusted by TOMRA, ZF, Komatsu, and other global machine manufacturers.