Fleetio Saves Customers 4+ Minutes Per Service Entry with bem's Unstructured Document Processing
Fleetio transforms manual data entry workflows, reducing service entry processing time from 6.5 minutes to 2 minutes while exceeding customer expectations for accuracy.
Overview
Fleetio is a leading fleet management and optimization platform that helps businesses track vehicle maintenance, inspections, and operations. With millions of documents uploaded weekly across their platform, Fleetio needed an AI solution to automate the transcription of paper-based invoices and inspection forms into structured digital records, addressing customer friction to adopting new technologies while maintaining the accuracy required for fleet operations.
The Challenge
Before implementing bem, Fleetio faced several challenges in serving their customer base:
Manual Data Entry Bottlenecks: Customers were spending significant time manually transcribing paper invoices from mechanics into Fleetio's system. "It was taking our customers about six and a half minutes to manually create a service entry," shared Chuck Drews, Group Product Manager at Fleetio.
Paper-First Customer Base: Fleetio's customers preferred traditional paper workflows. "Paper is one of our biggest competitors beyond any other software competitors," Chuck explained. The workforce was generally resistant to adopting new software and technology, but the Fleetio product and engineering teams knew it was needed to support customers more.
Previous AI Solutions Fell Short: Fleetio had experimented with another provider’s system, but it didn't support the line-item extraction capabilities they required for service entries and invoices.
The Solution: Implementing bem for Automated Document Processing
Fleetio chose bem to address their document processing challenges, implementing our platform to:
Automate Invoice Processing: bem's advanced AI transformation and routing capabilities enabled automatic extraction of service entry data from mechanic invoices, including line items, totals, and service details.
Maintain Paper Workflows: The solution allowed customers to continue using familiar paper processes while adding digital efficiency through photo upload and automatic transcription.
Ensure Financial Accuracy: Critical for service entries that act as receipts, bem's processing maintained precise total calculations and financial data extraction.
Support Multiple Document Types: Beyond service entries, the platform was designed to handle inspection forms and other fleet management documents.
Results and Impact
Since implementing bem, Fleetio has delivered significant value to their customers:
Dramatic Time Savings: "We found that between bem and what we're doing with our service task mapping AI that it's like two minutes. So we're saving them about four minutes per service entry," Chuck shared. This represents a 65% reduction in processing time.
Exceptional Accuracy: "The totals, they were accurate. Right. That was the biggest thing, and it's been spot on," noted the Fleetio team.
Exceeded Expectations: "We have not experienced any hiccups so far. The process has exceeded expectations as far as what we thought was going to happen," Chuck reported.
Massive Customer Time Savings: With some customers processing hundreds of entries monthly, the 4-minute savings per entry has translated to saving customers 90+ hours of manual work.
Seamless Integration: The solution integrated smoothly with Fleetio's existing workflows, requiring minimal customer behavior change while delivering maximum efficiency gains.
Scaling with bem
The integration of bem has fundamentally transformed how Fleetio's customers interact with fleet management data. By maintaining familiar paper workflows while adding AI-powered efficiency, Fleetio has achieved the rare combination of technological advancement and user adoption success.
Given bem has provided 100% accuracy for all totals flowing through from Fleetio customers (in addition to delivering measurable time savings), we’re excited to expand into other use cases and document types moving forward.
