Service & After-Sales · SAIP

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
IoT & CRM datatelemetria
AI modelsPdM · AD
Planning & TicketingEPI · NLP
Un ecosistema integrato
Service & After-Sales Intelligence Platform

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.

The SAIP modules
PdMPredictive Maintenance

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.

ADAnomaly Detection

Anomaly Detection

Proprietary algorithms analyse all variables simultaneously and produce a global health factor, catching emerging issues long before they become critical.

DMPDynamic Maintenance Planner

Dynamic Maintenance Planner

Optimisation that combines IoT data, forecasts, contracts, technician availability and SLAs into dynamic plans: fewer trips, grouped interventions, constraints respected.

EPIEngineer Performance Index

Engineer Performance Index

Fair, multidimensional assessment of Field Service Engineers: complexity, costs, recurring faults, time between interventions. Targeted training and optimal assignment.

ChatwolfVirtual Assistant

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.

NLPTicketing

Ticketing

Sentiment analysis, solution recommendation and smart assignment: intelligence is extracted from tickets, critical cases are prioritised and routed to the right technician.

Why SAIP

An integrated ecosystem, not isolated components

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.

Typical impact
−20%
unplanned downtime
−30%
MTTR — mean time to resolution
+15%
OEE — overall equipment effectiveness

Want to see how SAIP can transform your service operations?

We start from your installed base and your ticketing data. Email us at hello@statwolf.com and we’ll show you where SAIP creates value first.