Service-request approval automated with AI
Source: machine-learning service-request approval system.
Service requests were approved manually and could take up to seven days, creating queues during demand peaks. Maxxi built a system that assesses and approves a request as soon as it is opened, using a machine learning model trained on historical data. Manual work was cut by more than half, and responses became almost immediate.
Every request waited up to a week for manual analysis
Request volumes were high, analysis was manual and responses took up to seven days. During demand peaks, queues formed and delayed the people who needed approval to move forward.
A model that approves requests as they are opened
Maxxi built the intelligence to make an immediate decision based on historical manual decisions.
Roughly 25,000 previously reviewed requests trained the machine learning model.
The system assesses and approves a request when it is opened, without sending it through a queue.
Automatically collected data is used to monitor model performance.
Immediate approval with half the manual work
How can AI automate service-request approvals?
Every request was reviewed manually, responses took up to seven days and queues formed during demand peaks.
Requests were approved automatically when opened, cutting manual work by more than half.
Business impact
Manual work fell by 50%–55%, approval accuracy reached 85%–90% and response times dropped sharply. The team could focus on the cases that genuinely required human analysis.
High-volume, repeatable decisions are work for a model
When a decision is repeated thousands of times using clear criteria, keeping it manual costs time and creates queues. A model trained on the operation's own history can absorb the volume and return human analysis to the cases that matter.
Published 2026.
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