Logistics & Supply Chain
United Kingdom

Automated Billing Correction System

Developed an LLM-powered system to automate the identification and correction of billing errors, reducing manual processing time by 90%.

0 Reduction
From 10 hours/week to 1 hour/week
0
Single source of truth
0 Faster
From three weeks to a few days

Industry

Logistics & Supply Chain

Company Size

Small Business

Location

United Kingdom

Technologies

LangChain
HuggingFace
LangGraph
SageMaker AI
Python
AWS

The Challenge

This Managed Service Provider for IT services in the industry for over 30 years often struggled with billing errors in their customer support invoices. It was common for support engineers to misclassify billable and non-billable hours or miss out on certain charges altogether. The manual review and correction process was time-consuming and prone to human error, leading to delayed, backlogged invoicing and customer dissatisfaction. The client sought an automated solution to streamline the billing correction process, improve accuracy, and reduce the time spent on manual reviews. The solution allowed the client to utilise modern data science techniques to enhance their operational efficiency.

Our Solution

Designed a bespoke LLM workflow to automatically identify and correct billing errors in customer support invoices. The system integrated with existing billing and helpdesk software to streamline the correction process, reducing manual effort and improving accuracy. We implemented a feedback loop to continuously refine the model based on user input, ensuring ongoing improvements in performance.

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