Automated Billing Correction System

This Managed Service Provider (MSP) with over 30 years in the IT services industry struggled with persistent billing errors in customer support invoices.

Major pain points:

• Support engineers frequently misclassified billable vs. non-billable hours
• Missed charges resulted in revenue leakage
• Manual review process was extremely time-consuming (10 hours/week)
• Human error in manual corrections compounded issues
• Delayed and backlogged invoicing frustrated customers
• Billing cycles extended to three weeks on average

The client sought an automated solution to streamline the billing correction process, improve accuracy, and reduce manual review time whilst leveraging modern data science techniques.

We designed a bespoke agentic workflow powered by Large Language Models (LLMs) to automatically identify and correct billing errors.

Solution architecture:

• Data Ingestion: Automated extraction of data into data lakehouse (MS Fabric).
• Multiple Data Sources: Integrated data from helpdesk software, communication tools, and billing systems for comprehensive analysis.
• Data Preparation: Cleaned and structured data to ensure high-quality inputs for the LLM.
• Automated Error Detection: LLM-powered analysis of support tickets and time entries to identify billing inconsistencies
• Semantic Search Integration: Connected with helpdesk software to verify factual accuracy of billing classifications
• Intelligent Correction: Automated suggestion and application of corrections based on historical patterns
• Continuous Learning: Feedback loop to refine the model based on user input and corrections

The system reduced manual billing time from 10 hours per week to just 1 hour, whilst accelerating the billing cycle from three weeks to just a few days.