Invoice processing · Production

Ascend Properties automates maintenance invoice processing with Nanonets, saving over 80% in costs

The problem

Ascend Properties manually verified and uploaded vendor invoices for maintenance services. As the business grew 50% YoY and 5x over four years, the manual process would have required over 5 full-time employees and took six hours a day to run.

Workflow diagram · grounded in source
1
Vendor invoice upload
trigger
“vendors can upload invoices for maintenance services”
2
Invoice OCR data capture
ai_action
“Nanonet's AI tool- Invoice OCR captures information from invoices and sends it to Ascend properties”
3
AI field extraction
ai_action
“Ascend trained the AI to extract required information from the invoices”
4
Field validation checks
validation
“they perform checks to ensure if all fields are correctly populated and in line with expectations”
Reported outcome

Ascend saved over 80% in costs for invoice processing and reduced daily processing time from six hours to 10 minutes, while avoiding a proportional increase in headcount.

Reported metrics
Invoice processing cost savingsover 80%
Daily processing time before automationsix hours a day
Daily processing time after automation10 minutes
Headcount increase avoidedavoid such an increase in staff
Show all 7 reported metrics
invoice processing cost savingsover 80%
daily processing time before automationsix hours a day
daily processing time after automation10 minutes
headcount increase avoidedavoid such an increase in staff
company YoY growth (context)50% YoY
business growth over 4 years (context)5x in last 4 years
full-time employees required without automationover 5 full time employees
Reported stack
Invoice OCR
Source
https://nanonets.com/customer-success-story/ascend-properties-automates-property-maintenance-invoice-using-nanonets
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Ascend saved over 80% in costs for invoice processing and reduced daily processing time from six hours to 10 minutes, while avoiding a proportional increase in headcount.

What tools did this team use?

Invoice OCR.

What results were reported?

Invoice processing cost savings: over 80%; Daily processing time before automation: six hours a day; Daily processing time after automation: 10 minutes; Headcount increase avoided: avoid such an increase in staff (source-reported, not independently verified).

How is this invoice processing AI workflow structured?

Vendor invoice upload → Invoice OCR data capture → AI field extraction → Field validation checks.

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