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.
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.
Dynamic Maintenance Planner
Optimisation that combines IoT data, forecasts, contracts, technician availability and SLAs into dynamic plans: fewer trips, grouped interventions, constraints respected.
Engineer Performance Index
Fair, multidimensional assessment of Field Service Engineers: complexity, costs, recurring faults, time between interventions. Targeted training and optimal assignment.
Virtual Assistant
A virtual assistant trained on the client’s manuals and documentation: precise answers 24/7, less dependence on human intervention for recurring questions.
Ticketing
Sentiment analysis, solution recommendation and smart assignment: intelligence is extracted from tickets, critical cases are prioritised and routed to the right technician.
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 Chatwolf.
