From reactive to predictive after-sales
Statwolf SAIP combines advanced analytics, machine learning and automation to move service teams from reactive to proactive: less downtime, more efficient resources, a better customer experience.
SAIP bridges the gap between the information made available by IoT and traditional CRM systems and its transformation into operational decisions, with several intelligent modules working together.
From the signal in the field to the right intervention, with the right technician: three pillars working on the same data and talking to each other.
Understand the health of machines and installed base, continuously.
Predictive Maintenance
ML algorithms estimate when a component may degrade or fail: you act only when it truly matters, sharply reducing unplanned downtime and costs.
Anomaly Detection
Proprietary algorithms analyse all variables simultaneously and produce a global health factor, catching emerging issues long before they become critical.
Turn signals into optimal intervention plans and set the right priorities.
Optimisation Engine
Combines IoT data, forecasts, contracts, technician availability and SLAs into dynamic plans: fewer trips, grouped interventions, constraints respected.
Ticketing Intelligence
Sentiment analysis, solution recommendation and smart assignment: intelligence is extracted from tickets, critical cases are prioritised and routed to the right technician.
Execute well, with the right technician, and improve over time.
Engineer Scoring
Fair, multidimensional assessment of Field Service Engineers: complexity, costs, recurring faults, time between interventions. Targeted training and optimal assignment.
Field Assistant
The Agentwolf assistant trained on the client’s manuals and documentation: precise answers 24/7 supporting the technician, less dependence on human intervention for recurring cases.
An ecosystem where the modules talk to each other
Deploying isolated AI components in after-sales creates noise and misalignment. In SAIP every module shares the same platform and the same data, they communicate with each other and self-correct.
When EPI reveals that a technician excels at a category of problems, DMP starts assigning them those tasks. When NLP Ticketing identifies a pattern of recurring solutions, the team formalises it and improves Agentwolf.
Connected products and plants: field data feeding the suites
Telemetry from machines, devices and installed products — even when they are not industrial machines: connected devices, appliances, distributed plants. IoT data enters the platform through cloud, fog and edge architectures and feeds the analytical modules of the suites.
- Products installed at the customer: installed-base health and predictive after-sales.
- Production lines and plants: machine and process monitoring.
- Cloud, fog and edge enablement and IoT data integration: Statwolf Platform.
The same IoT data serves both suites: for line and plant monitoring see Manufacturing Intelligence.
