Accounts payable · Production

Leading real estate company processes 5,000 invoices/month at 90% accuracy with Vic.ai autonomous finance platform

The problem

An accounts payable team processed approximately 3,000 invoices a month through manual scanning and data entry, struggled to code invoices to the right renovation projects, had to manually check against renovation budgets before payment, and managed invoices in spreadsheets that caused frequent errors and lost time.

Workflow diagram · grounded in source
1
Invoice batch scan and import
trigger
“By batch-scanning and importing invoices into Vic.ai”
2
AI data ingestion and processing
ai_action
“data is now ingested and processed on behalf of the team”
3
GL and line-item prediction
ai_action
“the team has seen good improvements in GL and line-item level predictions”
4
Budget check against invoices
validation
“making it faster and easier for them to perform budget checks against the invoices”
Reported outcome

After deploying Vic.ai, the AP team can now process 5,000 invoices a month at an average of 90% accuracy, with good improvements in GL and line-item level predictions, and leadership reports high satisfaction with the platform.

Reported metrics
Invoice processing accuracy (header stat)94%
Invoice processing accuracy (body stat)90%
monthly invoice volume processed with Vic.ai5,000 invoices a month
GL and line-item prediction qualitygood improvements
Show all 5 reported metrics
invoice processing accuracy (header stat)94%
invoice processing accuracy (body stat)90%
monthly invoice volume processed with Vic.ai5,000 invoices a month
GL and line-item prediction qualitygood improvements
leadership satisfactionhighly satisfied
Reported stack
Vic.aiOracle Cloud
Source
https://www.vic.ai/resources/case-studies/leading-real-estate-company-saves-time-and-improves-accuracy-with-vic-ai
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

After deploying Vic.ai, the AP team can now process 5,000 invoices a month at an average of 90% accuracy, with good improvements in GL and line-item level predictions, and leadership reports high satisfaction with the…

What tools did this team use?

Vic.ai, Oracle Cloud.

What results were reported?

Invoice processing accuracy (header stat): 94%; Invoice processing accuracy (body stat): 90%; monthly invoice volume processed with Vic.ai: 5,000 invoices a month; GL and line-item prediction quality: good improvements (source-reported, not independently verified).

How is this accounts payable AI workflow structured?

Invoice batch scan and import → AI data ingestion and processing → GL and line-item prediction → Budget check against invoices.