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Case study · Leading AC brand

From reactive service calls to predictive maintenance scheduling.

Service CloudEinstein Prediction BuilderAgentforce
30-40%

reduction in unplanned breakdowns

The client

A leading air conditioning brand running Service Cloud, whose service predictability and technician planning still struggled during high-demand seasons.

The problem

Even with Service Cloud deployed, service stayed reactive: emergency visits, escalations, and SLA penalties piled up in peak season, and the business had no way to forecast which units were about to fail.

What we built
Sketch: wall calendar with maintenance checkmarks and a wrench hanging from a hook

Failure prediction engine

A model synthesizing unit age, usage intensity, repair history, and environmental data to score failure probability.

Preventive case triggers

Prediction scores flow back into Service Cloud, automatically triggering preventive cases.

Agent guidance

Agentforce recommends next actions to service agents based on the prediction.

Capacity planning dashboards

CRM Analytics dashboards for technician scheduling and revenue planning.

Stack
Service CloudEinstein Prediction BuilderAgentforceCRM Analytics
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